# AgentsCamp > Guides, an AI tool directory, and a plain-language glossary for developers, founders, marketers, designers, and analysts, plus installable Claude Code skills. Guides, a tool directory, and a glossary for anyone working with AI, plus agents, skills, and commands that are copy-paste/installable and format-validated for Claude Code. Each content page below has a clean Markdown twin at the same URL with a `.md` suffix. ## Guides Long-form guides and tutorials for building with AI coding agents. - [Building an MCP Server](https://agentscamp.com/guides/advanced/building-an-mcp-server.md): An accurate introduction to the Model Context Protocol: server anatomy, transports, and connecting a tool to Claude Code. - [Building Multi-Step Agent Workflows](https://agentscamp.com/guides/advanced/building-multi-step-workflows.md): Patterns for building multi-step agent workflows in Claude Code: decompose tasks, fan-out to parallel subagents, verify every step, and orchestrate. - [Building Agents with the Claude Agent SDK](https://agentscamp.com/guides/advanced/claude-agent-sdk-tutorial.md): A working tutorial for the Claude Agent SDK in TypeScript and Python — query(), tool permissions, custom in-process MCP tools, subagents, hooks, and auth. - [Running Claude Code in CI: Headless Mode & GitHub Actions](https://agentscamp.com/guides/advanced/claude-code-ci-github-actions.md): Claude Code without the terminal — claude -p flags, JSON and structured output, safe permission scoping, and the official GitHub Action responding to @claude. - [Codex Subagents: Parallel Work Without Losing Control](https://agentscamp.com/guides/advanced/codex-subagents.md): Use Codex subagents for parallel exploration, review, tests, and implementation — including decomposition, custom agents, permissions, and merge discipline. - [Jina Reader API: r.jina.ai and s.jina.ai Headers, Examples, and Limits](https://agentscamp.com/guides/advanced/jina-reader-api.md): How to use the Jina Reader API: r.jina.ai and s.jina.ai quickstart, every documented request header, curl recipes, rate limits, MCP setup, and self-hosting. - [LLM API Pricing in 2026: Every Major Model Compared](https://agentscamp.com/guides/advanced/llm-api-pricing-2026.md): Per-million-token prices for Claude, GPT, Gemini, DeepSeek, Mistral, and Grok — plus caching and batch discounts — verified against vendor pricing pages. - [LLM Context Windows Compared (2026)](https://agentscamp.com/guides/advanced/llm-context-windows-compared.md): Context windows and max output tokens across Claude, GPT, Gemini, DeepSeek, and Grok — the million-token era, what it costs, and what fits in practice. - [LLM Cost and Latency Engineering: Caching, Right-Sizing, and p95 Budgets](https://agentscamp.com/guides/advanced/llm-cost-latency-engineering.md): A practical playbook for cutting LLM cost and tail latency — caching, model right-sizing, prompt trimming, and enforced p95 budgets — without losing quality. - [LLM Gateways Compared: Portkey vs Helicone vs LiteLLM for Caching & Cost Control](https://agentscamp.com/guides/advanced/llm-gateways-compared.md): How Portkey, Helicone, and LiteLLM compare for caching, cost control, and observability — each one's 2026 status and which fits self-hosted vs. hosted. - [Multi-Agent Orchestration](https://agentscamp.com/guides/advanced/multi-agent-orchestration.md): Four patterns for coordinating multiple agents — fan-out, pipeline, orchestrator-worker, and verify/critic — and when each earns its overhead. - [Parallel Claude Code Sessions with Git Worktrees](https://agentscamp.com/guides/advanced/parallel-claude-code-worktrees.md): Run several Claude Code sessions at once without edits colliding — the built-in claude --worktree flag, .worktreeinclude, subagent isolation, and cleanup. - [Sandboxing AI-Generated Code: E2B vs Modal vs Daytona vs Vercel Sandbox](https://agentscamp.com/guides/advanced/sandboxing-ai-generated-code.md): Where should agent-written code run? E2B vs Modal vs Daytona vs Vercel Sandbox compared on isolation, persistence, and cost, plus rules for safe execution. - [Are Claude Skills Safe? A Security Review Checklist](https://agentscamp.com/guides/ai-safety/are-claude-skills-safe.md): Skills are an instruction supply chain: what can go wrong with third-party SKILL.md files, and the review checklist before installing or distributing one. - [Data Privacy for LLM Apps: Stop Leaking Sensitive Data](https://agentscamp.com/guides/ai-safety/data-privacy-for-llm-apps.md): Where LLM apps leak PII and secrets — prompts, logs, traces, vector stores, providers — and the controls (redaction, ZDR, tenant isolation) that stop it. - [Defending Against Prompt Injection: A Practical Guide for LLM Apps](https://agentscamp.com/guides/ai-safety/defending-prompt-injection.md): Prompt injection can't be solved at the model layer — so you defend in depth: trust boundaries, least privilege, human approval, guardrails, and red-teaming. - [Securing AI Agents: The OWASP Agentic Top 10 in Practice](https://agentscamp.com/guides/ai-safety/owasp-agentic-top-10.md): Agents add risks LLM-app security misses — autonomy, tools, memory, multi-agent trust. The key OWASP agentic threats and how to mitigate each in practice. - [Red-Teaming LLM Applications: From Attack Cases to Regression Tests](https://agentscamp.com/guides/ai-safety/red-teaming-llm-applications.md): Red-team an LLM application across prompts, RAG, tools, agents, and data boundaries — scope assets, generate attacks, score impact, fix, and retest in CI. - [Software Supply Chain Security: Dependencies, Builds, and Releases](https://agentscamp.com/guides/ai-safety/software-supply-chain-security.md): Secure the software supply chain from dependency selection through release — lockfiles, provenance, least privilege, secret controls, SBOMs, and response. - [Review AI Duplicate-Record Candidates Before Merging](https://agentscamp.com/guides/analytics/ai-duplicate-record-review.md): Separate duplicate-record candidate generation from human match decisions, retaining source keys and field evidence before any merge. - [How to Check an AI Data Analysis Before You Trust It](https://agentscamp.com/guides/analytics/check-an-ai-data-analysis.md): Eight ways an AI-generated analysis goes wrong, the specific check that catches each one, and a copyable checklist to run before a number ships. - [Claude Code for Data Analysts: Notebooks, SQL, and CSV Work Without a Data Engineer](https://agentscamp.com/guides/analytics/claude-code-for-data-analysts.md): Run analysis work in Claude Code: an analysis repo with CLAUDE.md, a CSV profiling pass, a pandas loop, a read-only warehouse query, and permission rules. - [Anthropic's Data Plugin for Claude: Every Skill Explained](https://agentscamp.com/guides/analytics/claude-data-plugin-guide.md): Every skill in Anthropic's open-source data plugin for Claude Cowork and Claude Code, the warehouse connectors it expects, the install commands, and its gaps. - [Claude for Data Analysis: The Complete 2026 Guide](https://agentscamp.com/guides/analytics/claude-for-data-analysis.md): The four surfaces where Claude touches data, what the code execution sandbox really is, how an AI analysis fails silently, and the checks that catch it. - [Claude for Excel: What It Does, Where It Fails, How to Use It](https://agentscamp.com/guides/analytics/claude-for-excel-guide.md): Install it from AppSource, learn which Excel builds are supported, use cell-level citations, and apply the review discipline before a workbook leaves your desk. - [Claude Skills for Data Analysts: 5 to Upload Today](https://agentscamp.com/guides/analytics/claude-skills-for-data-analysts.md): Five portable skills that make Claude behave like a careful analyst: first look, chart choice, memo writing, SQL explanation, and formula auditing. - [Link Records Across Datasets with AI-Assisted Review](https://agentscamp.com/guides/analytics/link-records-across-datasets.md): Build a cross-source entity crosswalk that preserves both record keys, unresolved candidates and cardinality decisions without silently joining or merging data. - [Text-to-SQL with Claude: A Safe Read-Only Postgres Setup](https://agentscamp.com/guides/analytics/text-to-sql-with-claude.md): Set up text-to-SQL with Claude on Postgres safely: a read-only role, an MCP server in read-only mode, schema and metric context, and a two-way check. - [Which Claude Plan (and Model) Should a Data Analyst Pay For?](https://agentscamp.com/guides/analytics/which-claude-plan-for-data-analysts.md): A decision guide by situation: occasional CSV work, daily Excel modeling, Claude Code against a warehouse, or a team sharing a Project. No prices. - [Idempotency Keys: Design APIs That Are Safe to Retry](https://agentscamp.com/guides/api/idempotency-keys-api-design.md): Design idempotent API mutations with client keys, atomic claims, response replay, payload fingerprints, in-flight handling, TTLs, and downstream safety. - [Aider vs Claude Code: Open-Source vs Anthropic's Agent (2026)](https://agentscamp.com/guides/comparisons/aider-vs-claude-code.md): Aider vs Claude Code — model-agnostic open-source pair-programmer vs Anthropic's tuned terminal harness. Which terminal coding agent fits your stack. - [Base44 vs Lovable: Which AI App Builder for Non-Technical Founders?](https://agentscamp.com/guides/comparisons/base44-vs-lovable.md): Base44 vs Lovable for non-technical founders: Base44's all-in-one managed backend and hosting versus Lovable's Supabase stack with GitHub sync. Verdict first. - [Best AI App Builders in 2026: v0 vs Lovable vs Bolt vs Replit](https://agentscamp.com/guides/comparisons/best-ai-app-builders-2026.md): Seven AI app builders compared for 2026 — v0, Lovable, Bolt, Replit Agent, Base44, Emergent, and Rork — with a verdict, a pricing model, and a best-for each. - [Best AI Code Review Tools in 2026](https://agentscamp.com/guides/comparisons/best-ai-code-review-tools-2026.md): The AI code reviewers worth running in 2026 — CodeRabbit, Greptile, Qodo, Cursor Bugbot, and GitHub Copilot code review compared, with a verdict for each. - [The Best AI Image Generators in 2026 (for Design Work)](https://agentscamp.com/guides/comparisons/best-ai-image-generators-2026.md): AI image generators compared for design work in 2026: text rendering, vector output, style control, commercial licensing, API access, and pricing model. - [The Best AI Sales Tools in 2026](https://agentscamp.com/guides/comparisons/best-ai-sales-tools-2026.md): Ten AI sales tools re-verified in September 2026: what each one does, whether Claude can connect to it, and how the AI is metered apart from the seat. - [The Best AI Tools for Data Analysts in 2026](https://agentscamp.com/guides/comparisons/best-ai-tools-for-data-analysts-2026.md): The AI tools data analysts actually use in 2026: assistants that run code, spreadsheet add-ins, notebooks, text-to-SQL, and agents in the terminal. - [The Best AI Tools for Designers in 2026](https://agentscamp.com/guides/comparisons/best-ai-tools-for-designers-2026.md): The AI tools designers actually use in 2026: prototyping, websites, image generation, decks, design-to-code, and design systems, with a verdict per category. - [The Best AI Tools for Founders in 2026](https://agentscamp.com/guides/comparisons/best-ai-tools-for-founders-2026.md): The AI tools founders run a company on in 2026: assistants, app builders, agents and automation, decks and docs, and building, with a verdict per category. - [The Best AI Tools for Marketers in 2026](https://agentscamp.com/guides/comparisons/best-ai-tools-for-marketers-2026.md): The AI tools marketers run a content program on in 2026: assistants, writing, SEO, research, decks, video, and automation, with a verdict per category. - [The Best AI Writing Tools in 2026](https://agentscamp.com/guides/comparisons/best-ai-writing-tools-2026.md): Six AI writing tools compared by job: long-form, ad copy, email, and writing inside Docs or Word. Jasper, Copy.ai, Claude, ChatGPT, Gemini, and Copilot. - [The Best Claude Code Alternatives in 2026 (Free, Open-Source, and Paid)](https://agentscamp.com/guides/comparisons/best-claude-code-alternatives-2026.md): The best Claude Code alternatives in 2026, sorted by why you're leaving: cost, open source, local models, an IDE, cloud agents, or OpenAI and Google. - [The Best Document Parsers for RAG in 2026: LlamaParse vs Docling vs Unstructured vs Reducto](https://agentscamp.com/guides/comparisons/best-document-parsers-for-rag-2026.md): LlamaParse, Docling, Unstructured, Reducto, Marker and Mistral OCR compared on deployment, license, OCR, tables and output, with a verdict for each. - [The Best GitHub Copilot Alternatives in 2026 (Free and Paid)](https://agentscamp.com/guides/comparisons/best-github-copilot-alternatives-2026.md): The best GitHub Copilot alternatives in 2026: AI-native editors, open-source VS Code and JetBrains agents, self-hosted assistants and terminal agents. - [The Best LLM Inference Providers in 2026: Groq vs Together vs Fireworks vs Baseten and More](https://agentscamp.com/guides/comparisons/best-llm-inference-providers-2026.md): Groq, Cerebras, Together AI, Fireworks AI, Baseten, Modal, fal and Replicate compared by workload, with 2026 ownership changes and OpenAI compatibility. - [The Best LLM Observability Tools in 2026: Langfuse vs LangSmith vs Braintrust vs Phoenix](https://agentscamp.com/guides/comparisons/best-llm-observability-tools-2026.md): Langfuse, LangSmith, Braintrust, Arize Phoenix, Helicone and AgentOps compared on tracing, cost tracking, agent debugging, OpenTelemetry and self-hosting. - [Best Tools for Running LLMs Locally in 2026](https://agentscamp.com/guides/comparisons/best-local-llm-tools-2026.md): The local LLM stack ranked by job — Ollama, LM Studio, Jan, llama.cpp, vLLM, and LiteLLM — with a verdict each and the line where local inference stops paying. - [Best RAG Frameworks in 2026](https://agentscamp.com/guides/comparisons/best-rag-frameworks-2026.md): The RAG frameworks worth building on in 2026 — LlamaIndex, LangChain, LangGraph, DSPy, Dify and Mastra — plus the retrieval layer that decides answer quality. - [The Best Text-to-SQL Tools in 2026](https://agentscamp.com/guides/comparisons/best-text-to-sql-tools-2026.md): Text-to-SQL tools compared on how they ground the model in your schema, what accuracy really means, read-only safety, deployment, licensing, and pricing model. - [Best Web Search APIs for AI Agents in 2026: Tavily vs Exa vs Brave vs Parallel](https://agentscamp.com/guides/comparisons/best-web-search-apis-for-ai-agents-2026.md): Tavily, Exa, the Brave Search API, Parallel, Jina Reader and Firecrawl compared on search, extraction, answers, index ownership, and official MCP servers. - [Browser Agents in 2026: Browser Use vs Stagehand vs Skyvern vs Playwright MCP](https://agentscamp.com/guides/comparisons/browser-agents-compared-2026.md): Seven ways to give AI a browser — Browser Use, Stagehand, Skyvern, the MCP servers, Claude in Chrome, Browserbase and Firecrawl — compared with a verdict each. - [ChatGPT Projects vs Claude Projects for Teams](https://agentscamp.com/guides/comparisons/chatgpt-projects-vs-claude-projects.md): Compare shared AI project context in ChatGPT and Claude, then build a team brief with clear instructions, source ownership, and a review process. - [ChatGPT vs Claude for Data Analysis](https://agentscamp.com/guides/comparisons/chatgpt-vs-claude-for-data-analysis.md): ChatGPT vs Claude for data analysis, compared on sandbox behavior, file limits, Excel add-ins, chart output, citations, warehouse access, and verification. - [Claude Code vs Codex CLI: Terminal Agents Compared (2026)](https://agentscamp.com/guides/comparisons/claude-code-vs-codex-cli.md): Claude Code vs OpenAI's Codex CLI — autonomy vs sandboxed control, extensibility vs open source, model ecosystems, and which terminal agent fits your work. - [Claude Code vs Cursor: Which AI Coding Tool in 2026?](https://agentscamp.com/guides/comparisons/claude-code-vs-cursor.md): Claude Code vs Cursor compared honestly — terminal agent vs AI-first editor, autonomy vs inline control, pricing models, and when to run both. - [Claude Code vs Gemini CLI: Which Terminal Agent (2026)](https://agentscamp.com/guides/comparisons/claude-code-vs-gemini-cli.md): Claude Code vs Gemini CLI: first-party stability and a deep programmable harness vs open-source TypeScript, a big free tier, and the Antigravity cutover. - [Claude Code vs OpenCode: First-Party vs Open Source (2026)](https://agentscamp.com/guides/comparisons/claude-code-vs-opencode.md): Claude Code vs OpenCode — Anthropic's tuned first-party agent vs the most-starred open-source one with 75+ providers. Control vs polish, decided honestly. - [Claude Design vs Figma Make: Which Prompt-to-Prototype Tool?](https://agentscamp.com/guides/comparisons/claude-design-vs-figma-make.md): Claude Design vs Figma Make compared on what each makes, plan and seat requirements, design-system input, refinement, export, and handoff to code. - [Claude Skills vs Custom GPTs: Different Answers to Reuse](https://agentscamp.com/guides/comparisons/claude-skills-vs-custom-gpts.md): Claude Skills are portable procedures; Custom GPTs are packaged chatbots inside ChatGPT. How they differ on portability, API access, sharing, and where each wins. - [Claude vs ChatGPT for Writing and Marketing Copy](https://agentscamp.com/guides/comparisons/claude-vs-chatgpt-for-writing.md): Claude vs ChatGPT for writing, compared on voice control, document creation, research grounding, collaboration, brand-voice enforcement, and agentic work. - [Clay vs Apollo: Which GTM Data Tool in 2026?](https://agentscamp.com/guides/comparisons/clay-vs-apollo.md): Clay vs Apollo compared on what each actually is, the two-meter versus one-seat pricing shape, the MCP surface Claude can drive, and the fine print on credits. - [Cline vs Kilo Code vs Roo Code (2026): Which to Use After Roo's Shutdown](https://agentscamp.com/guides/comparisons/cline-vs-kilo-code-vs-roo-code.md): Roo Code shut down in May 2026. Cline vs Kilo Code compared on license, agents, checkpoints, BYOK and pricing model, plus how Roo users migrate. - [Cursor vs Windsurf (Devin Desktop) in 2026](https://agentscamp.com/guides/comparisons/cursor-vs-windsurf.md): Cursor vs Windsurf — now Devin Desktop — compared: agent-first editing, Composer vs Devin Local, the Cognition rebrand, and which AI editor fits you. - [DeepEval vs RAGAS: LLM Evaluation Frameworks Compared (2026)](https://agentscamp.com/guides/comparisons/deepeval-vs-ragas.md): DeepEval vs RAGAS — pytest-style general LLM testing vs RAG-specialized metrics. Which open-source eval framework fits your pipeline, or whether you need both. - [Exa vs Tavily: Web Search APIs for AI Agents (2026)](https://agentscamp.com/guides/comparisons/exa-vs-tavily.md): Exa vs Tavily compared — neural semantic discovery vs agent-optimized RAG answers, pricing, MCP support, and which web search API fits your stack. - [GitHub Copilot vs Cursor: Extension or Editor? (2026)](https://agentscamp.com/guides/comparisons/github-copilot-vs-cursor.md): GitHub Copilot vs Cursor compared — stay in your editor with an extension, or switch to an AI-first fork? Completion, agents, enterprise fit, and pricing shape. - [LangChain vs LlamaIndex in 2026: Agents or Data?](https://agentscamp.com/guides/comparisons/langchain-vs-llamaindex.md): The classic framework confusion resolved — LangChain's agent loop and ecosystem vs LlamaIndex's data-and-documents depth — and when you'd genuinely use both. - [Langfuse vs LangSmith: LLM Observability Compared (2026)](https://agentscamp.com/guides/comparisons/langfuse-vs-langsmith.md): Langfuse vs LangSmith — open-source self-hostable observability vs LangChain's first-party platform. Tracing, evals, prompt management, and which to adopt. - [LangGraph vs CrewAI: Agent Frameworks Compared (2026)](https://agentscamp.com/guides/comparisons/langgraph-vs-crewai.md): LangGraph vs CrewAI — explicit state-machine control vs role-based crew abstractions. Which agent framework fits your reliability bar and team. - [LiteLLM vs OpenRouter: One API for Every Model (2026)](https://agentscamp.com/guides/comparisons/litellm-vs-openrouter.md): LiteLLM vs OpenRouter compared — self-hosted gateway library vs hosted model marketplace. Keys, billing, control, and which unified LLM layer fits. - [Lovable vs Claude Code: Builder or Agent?](https://agentscamp.com/guides/comparisons/lovable-vs-claude-code.md): Lovable vs Claude Code for founders: the chat app builder that gives you a working product versus the coding agent that changes a real codebase. Verdict first. - [Mastra vs LangChain (2026): TypeScript Agent Frameworks Compared](https://agentscamp.com/guides/comparisons/mastra-vs-langchain.md): Mastra vs LangChain.js and LangGraph.js in 2026: license, workflows, memory, evals, observability and deployment compared, plus where the Vercel AI SDK fits. - [Mem0 vs Zep vs Letta: Agent Memory Compared (2026)](https://agentscamp.com/guides/comparisons/mem0-vs-zep-vs-letta.md): Three philosophies of agent memory — Mem0's drop-in layer, Zep's temporal knowledge graphs, Letta's self-managing agents — and which fits your architecture. - [Microsoft 365 Copilot vs ChatGPT for Work](https://agentscamp.com/guides/comparisons/microsoft-365-copilot-vs-chatgpt-for-work.md): Compare Microsoft 365 Copilot and ChatGPT for workplace tasks by source access, app context, deliverables, and a practical team pilot. - [n8n vs Dify: Which AI Workflow Platform? (2026)](https://agentscamp.com/guides/comparisons/n8n-vs-dify.md): Automation-first vs AI-native: n8n's 400+ integrations and agent nodes vs Dify's LLM-app platform with built-in RAG. Licenses, pricing, and the fit test. - [Ollama vs LM Studio: Running LLMs Locally (2026)](https://agentscamp.com/guides/comparisons/ollama-vs-lm-studio.md): Ollama vs LM Studio compared — CLI-first server for developers vs polished desktop app for exploring local models. Which local LLM tool fits how you work. - [OpenAI Agents SDK vs LangGraph: Minimal vs Controllable (2026)](https://agentscamp.com/guides/comparisons/openai-agents-sdk-vs-langgraph.md): OpenAI Agents SDK's three-primitive minimalism vs LangGraph's explicit graph and durable state — which agent framework matches your reliability bar in 2026. - [pgvector vs Pinecone: Do You Need a Vector Database? (2026)](https://agentscamp.com/guides/comparisons/pgvector-vs-pinecone.md): pgvector vs Pinecone compared — vector search inside the Postgres you already run vs a dedicated managed service. Scale thresholds, ops, and the honest default. - [Qdrant vs Pinecone: Which Vector Database? (2026)](https://agentscamp.com/guides/comparisons/qdrant-vs-pinecone.md): Qdrant vs Pinecone compared — open-source control vs fully managed serverless, filtering and hybrid search, cost shape, and which fits your RAG stack. - [Surfer vs Clearscope: Which SEO Content Tool in 2026?](https://agentscamp.com/guides/comparisons/surfer-vs-clearscope.md): Surfer vs Clearscope compared on the content editor, briefs, audits, AI writing, AI-visibility tracking, integrations, and pricing model, with a verdict first. - [v0 vs Lovable: AI App Builders Compared (2026)](https://agentscamp.com/guides/comparisons/v0-vs-lovable.md): v0 vs Lovable — Vercel's generative UI tool vs the full-app builder. Component quality vs end-to-end apps, code ownership, and who each serves best. - [vLLM vs Ollama: Local Convenience or Serving Throughput? (2026)](https://agentscamp.com/guides/comparisons/vllm-vs-ollama.md): vLLM vs Ollama compared — developer-friendly local runtime vs high-throughput production inference engine. Concurrency, hardware, and when to graduate. - [Weaviate vs Pinecone: Open-Source vs Managed Vector DB (2026)](https://agentscamp.com/guides/comparisons/weaviate-vs-pinecone.md): Weaviate vs Pinecone — BSD-3 open source you self-host vs fully managed serverless. Hybrid search, scaling, cost shape, and which fits your RAG stack. - [Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDK](https://agentscamp.com/guides/concepts/agent-frameworks-2026.md): A decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project. - [Agent Memory Architecture: Short-Term, Long-Term, and When to Use Each](https://agentscamp.com/guides/concepts/agent-memory-architecture.md): How AI agents remember — working memory vs. persistent long-term memory, what to store, how to retrieve it, and how to keep context small. - [Agentic RAG: When Retrieval Needs an Agent in the Loop](https://agentscamp.com/guides/concepts/agentic-rag.md): What agentic RAG is — retrieval as a tool an agent uses iteratively, with query planning, self-correction, and multi-source routing — and when the upgrade pays. - [AI Coding Statistics 2026: The Numbers That Are Actually Sourced](https://agentscamp.com/guides/concepts/ai-coding-statistics-2026.md): How much code AI writes, who uses the tools, and what it does to quality — every statistic dated and traced to its primary source, updated on a cadence. - [Calling Any Model: Unified LLM Gateways & SDKs in 2026](https://agentscamp.com/guides/concepts/calling-any-model-gateways.md): Why teams put a unified layer in front of LLM providers — and how LiteLLM, OpenRouter, and the Vercel AI SDK compare for fallback and cost control. - [Choosing Embeddings in 2026: OpenAI vs Cohere vs Voyage vs Open-Source](https://agentscamp.com/guides/concepts/choosing-embeddings-2026.md): A decision guide for picking an embedding model for retrieval — accuracy, dimensions, cost, multilingual and domain fit, self-hosting, and lock-in. - [GraphRAG Explained: When Knowledge Graphs Beat Vector Search](https://agentscamp.com/guides/concepts/graph-rag.md): What GraphRAG is, how graph-based retrieval differs from vector RAG, the query shapes where it wins, and the honest costs before you build one. - [How Computer-Use Agents Work](https://agentscamp.com/guides/concepts/how-computer-use-agents-work.md): Inside the perception-action loop that lets AI operate real software — screenshots in, clicks out — plus grounding, reliability, and when to use APIs instead. - [How Embeddings Work: Vectors, Similarity, and Choosing a Model](https://agentscamp.com/guides/concepts/how-embeddings-work.md): What an embedding actually is, how similarity is measured, how the models are trained, and the practical rules for using embeddings well in search and RAG. - [How RAG Actually Works: Ingestion, Chunking, Retrieval & Reranking](https://agentscamp.com/guides/concepts/how-rag-works.md): A clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together. - [Hybrid Search & Reranking: From Top-50 Recall to Top-5 Precision](https://agentscamp.com/guides/concepts/hybrid-search-reranking.md): How production RAG combines dense and sparse search, fuses with RRF, and reranks — turning a wide candidate set into the few passages that actually answer. - [Production Model Routing: Cut Cost Without Hiding Regressions](https://agentscamp.com/guides/concepts/production-model-routing.md): Design an LLM model router with capability gates, difficulty signals, cascades, fallbacks, per-route evals, shadow traffic, budgets, and safe rollout. - [Production Tool & Function Calling: Feed Errors Back as Observations](https://agentscamp.com/guides/concepts/production-tool-calling.md): How agents use tools — the call/observe/retry loop, why errors must return to the model, and the schemas, idempotency, and limits that keep it reliable. - [RAG vs Long Context: Do Million-Token Windows Kill Retrieval?](https://agentscamp.com/guides/concepts/rag-vs-long-context.md): Million-token context windows promised the end of RAG. The honest 2026 answer: long context changed where retrieval starts paying, not whether it does. - [Structured Output vs JSON Mode vs Function Calling: Which to Use in 2026](https://agentscamp.com/guides/concepts/structured-output-2026.md): The reliable ways to get typed data out of an LLM — what JSON mode, function calling, and native structured outputs each guarantee, and when to use which. - [Getting Web Data into AI Agents: Search & Scraping APIs Compared](https://agentscamp.com/guides/concepts/web-data-for-ai-agents.md): The agent web-data layer — Exa for semantic search, Firecrawl for extraction at scale, Tavily for all-in-one, Jina Reader for zero-setup — and how they compose. - [Claude Code Hooks: Automate Formatting, Tests, and Guardrails](https://agentscamp.com/guides/configuration/claude-code-hooks.md): How Claude Code hooks work — the major hook events, the settings.json configuration shape, exit codes and JSON output, plus three hooks worth copying. - [Managing Claude Code Memory & Context: CLAUDE.md, /compact, and Auto-Memory](https://agentscamp.com/guides/configuration/claude-code-memory-context.md): How Claude Code remembers — every CLAUDE.md scope and load order, path-scoped rules, the auto-memory system, and the context commands that keep sessions sharp. - [Claude Code Plugins: Install, Use, and Build Your Own](https://agentscamp.com/guides/configuration/claude-code-plugins.md): How Claude Code plugins work — what they can bundle, the /plugin and marketplace commands, the plugin.json manifest, and building and testing your own. - [Claude Code Settings & Permissions: settings.json Explained](https://agentscamp.com/guides/configuration/claude-code-settings-permissions.md): Every Claude Code settings file and which one wins, the permission-rule syntax with its Bash matching gotchas, modes, and a safe starter settings.json. - [Claude Code Slash Commands: The Built-in List and How to Write Your Own](https://agentscamp.com/guides/configuration/claude-code-slash-commands.md): Claude Code's built-in slash commands grouped by job, and how to write your own as skills: file format, frontmatter, arguments, and plugin namespacing. - [CLAUDE.md Best Practices](https://agentscamp.com/guides/configuration/claude-md-best-practices.md): How to write a CLAUDE.md that actually helps — what to include, what to leave out, and how to keep it current. - [AGENTS.md for Codex: Project Instructions That Actually Work](https://agentscamp.com/guides/configuration/codex-agents-md.md): Write an effective AGENTS.md for OpenAI Codex — what belongs in it, how nested overrides work, and how to verify the instructions Codex loaded. - [Codex config.toml: Settings, Precedence, and Safe Defaults](https://agentscamp.com/guides/configuration/codex-config-toml.md): Configure OpenAI Codex with config.toml — user and project scopes, precedence, sandbox and approvals, MCP, subagents, profiles, and safe defaults. - [Codex CLI Sandbox and Approvals in 2026: Auto-Approve, Full Access, Web Search](https://agentscamp.com/guides/configuration/codex-sandbox-and-approvals.md): Set Codex CLI's approval policy and sandbox mode: auto-approve without --full-auto, safe full access, network access, web search, profiles, and fixes. - [Adjudicate AI-Assisted Text Labels with an Evidence Ledger](https://agentscamp.com/guides/data/adjudicate-ai-assisted-text-labels.md): Resolve disputed text labels by preserving each response, source excerpt and task-rule version rather than accepting the model or majority automatically. - [Prelabel a Text Dataset with AI Without Replacing Review](https://agentscamp.com/guides/data/ai-text-dataset-prelabeling.md): Prepare a small supervised text-labeling batch with separate model suggestions and human responses, using stable record IDs and an approved task specification. - [Best Vector Database in 2026: pgvector vs Pinecone vs Qdrant vs Weaviate vs Milvus vs Chroma vs LanceDB](https://agentscamp.com/guides/database/best-vector-database-2026.md): A decision guide to vector databases — embedded, server, or managed; whether you already run Postgres; and which fits your scale, filtering, and RAG needs. - [Postgres Connection Pooling: Sizing, Timeouts, and Serverless](https://agentscamp.com/guides/database/postgres-connection-pooling-guide.md): Size Postgres connection pools across application instances, configure lifecycle timeouts, diagnose saturation, and choose PgBouncer or a managed pooler. - [Indexing Postgres at Scale: B-Tree vs GIN vs BRIN and the Hidden Cost of Over-Indexing](https://agentscamp.com/guides/database/postgres-indexing-at-scale.md): A practical guide to choosing Postgres index types — B-Tree, GIN, BRIN, partial, and covering — and why every index you add taxes every write. - [Vector Search at Scale: ANN Indexes, Quantization & Sharding](https://agentscamp.com/guides/database/vector-search-at-scale.md): How to run vector search over millions to billions of vectors without blowing latency, memory, or cost — index families, quantization, filtering, and sharding. - [Zero-Downtime Postgres Migrations: The Expand-Contract Playbook for 2026](https://agentscamp.com/guides/database/zero-downtime-postgres-migrations.md): How to change a live Postgres schema without downtime or broken deploys — the expand-contract pattern, safe column changes, batched backfills, and CONCURRENTLY. - [Review AI Alt Text in the Context of Its Page](https://agentscamp.com/guides/design/ai-alt-text-context-review.md): Draft image text alternatives from actual image evidence and page purpose, then review informative, decorative and functional uses before publishing. - [Draft AI Chart Long Descriptions from Verified Data](https://agentscamp.com/guides/design/ai-chart-long-descriptions.md): Review a chart-description draft against the frozen source table, actual visual observations and its implemented page placement. - [Create an AI Explanatory Diagram from Approved Text](https://agentscamp.com/guides/design/ai-explanatory-diagram-from-text.md): Choose a diagram that preserves the relationships in approved text, then inspect labels, arrows and the exported visual before reuse. - [Plan an AI Storyboard from an Approved Script](https://agentscamp.com/guides/design/ai-storyboard-from-script.md): Map an approved script into AI-assisted storyboard shots with beat IDs, visual constraints, continuity checks and a human review before production. - [Claude Code for Designers: Prototype in Code Without Becoming an Engineer](https://agentscamp.com/guides/design/claude-code-for-designers.md): Why a designer opens a terminal agent, how to set it up safely, connecting Figma, a CLAUDE.md for a design-system repo, and three workflows with prompts. - [Claude Design: The Complete Guide (2026)](https://agentscamp.com/guides/design/claude-design-guide.md): What Claude Design makes, how it ingests your design system, the three editing modes, every export and handoff, and where it stops. Written for designers. - [Anthropic's Design Plugin for Claude: Every Skill Explained](https://agentscamp.com/guides/design/claude-design-plugin-guide.md): Every skill in Anthropic's open-source design plugin for Claude Cowork and Claude Code, the connectors it expects, the install commands, and what it leaves out. - [Claude Skills for Designers: 5 to Upload Today](https://agentscamp.com/guides/design/claude-skills-for-designers.md): Five Claude skills for designers: brief, critique, token extraction, component spec, and UX copy review. What each returns, a prompt for each, and upload steps. - [Figma to Code with Claude Code and the Figma MCP Server](https://agentscamp.com/guides/design/figma-to-code-with-claude.md): Connect Figma's MCP server to Claude Code and turn a selected frame into a component that uses your real design tokens and your existing components. - [Maintaining a Design System with Claude Code](https://agentscamp.com/guides/design/maintain-a-design-system-with-claude-code.md): Tokens, components, docs, and the Figma file drift apart within weeks. A repo layout, CLAUDE.md rules, and three Claude Code jobs that catch it every week. - [Review AI-Generated 3D Meshes Before Asset Handoff](https://agentscamp.com/guides/design/review-ai-generated-3d-meshes.md): Evaluate an AI-generated mesh against a named target app and supplied asset requirements, using real inspection reports and human view checks. - [Review AI PBR Texture Imports Against the Target Material](https://agentscamp.com/guides/design/review-ai-pbr-texture-imports.md): Check an AI texture set in its actual target material setup, preserving map roles, UV information and viewer evidence before approving the asset. - [Which Claude Plan Should a Designer Pay For?](https://agentscamp.com/guides/design/which-claude-plan-for-designers.md): A decision guide by situation: solo prototyping, a team sharing one design system, heavy Design and Cowork use, or an agency. Plan names, no prices. - [Best LLM & RAG Evaluation Tools in 2026: DeepEval vs RAGAS vs LangSmith vs Phoenix vs promptfoo](https://agentscamp.com/guides/evaluation/best-llm-eval-tools-2026.md): A decision guide to the LLM eval landscape — code-first frameworks vs. eval-and-observability platforms, open-source vs. hosted, and which fits your stack. - [LLM-as-Judge: Build Evaluators You Can Actually Trust](https://agentscamp.com/guides/evaluation/llm-as-judge-guide.md): Design and calibrate LLM-as-judge evaluators — rubrics, pointwise and pairwise scoring, bias controls, human agreement, reliability tests, and CI gates. - [LLM Evaluation Metrics Explained: Which One to Use and When](https://agentscamp.com/guides/evaluation/llm-evaluation-metrics-explained.md): A practical map of LLM and RAG evaluation metrics — why BLEU/ROUGE fail open-ended text, how LLM-as-judge and RAG metrics work, and which to pick per task. - [Write Evals for an LLM App: From Zero to a CI Gate](https://agentscamp.com/guides/evaluation/write-llm-evals.md): How to evaluate an LLM feature — build a dataset, choose metrics, set a baseline, score offline, add an LLM judge, and gate CI so quality changes are measured. - [Build an Interview Scorecard with AI from Job Evidence](https://agentscamp.com/guides/founders/ai-interview-scorecard.md): Build an interview scorecard with AI using approved job tasks, observable competencies, shared questions, rating anchors, and human rubric review. - [Automating Startup Ops Without Engineers: Cowork, n8n, Zapier Agents, Lindy](https://agentscamp.com/guides/founders/automate-startup-ops-with-ai-agents.md): Four ways to automate startup ops without an engineer: Claude Cowork, n8n, Zapier Agents, and Lindy compared, with a worked example each and the approval step. - [Build an MVP with Claude Code: Idea to Deployed App in a Weekend](https://agentscamp.com/guides/founders/build-an-mvp-with-claude-code.md): A non-developer's weekend plan for an MVP with Claude Code: one-page spec, a boring stack, tests as you go, Supabase for auth and data, Vercel to deploy. - [Claude Code for Non-Developers and Founders: The Complete 2026 Guide](https://agentscamp.com/guides/founders/claude-code-for-non-developers.md): What Claude Code actually is in plain language, why non-developers use it, how permissions and CLAUDE.md work, and the first three tasks a founder should try. - [Claude for Founders: Which Claude Product for Which Job](https://agentscamp.com/guides/founders/claude-for-founders.md): A surface map for founders: when to use claude.ai, Claude Design, Cowork, Claude Code, Claude for Excel, and Claude in Chrome, with a job-to-product table. - [Claude Skills for Founders: 5 to Upload Today](https://agentscamp.com/guides/founders/claude-skills-for-founders.md): Five Claude skills built for founders (PRD, MVP scope, interview synthesis, competitor teardown, investor update), how to upload them, and a prompt for each. - [When Your Lovable or Bolt App Outgrows the Builder](https://agentscamp.com/guides/founders/lovable-to-claude-code-handoff.md): Signs your Lovable or Bolt app has outgrown its builder, how GitHub sync gives you a repository, and what to audit first with Claude Code: auth, secrets, RLS. - [Which Claude Plan (and Model) Should a Founder Pay For?](https://agentscamp.com/guides/founders/which-claude-plan-for-founders.md): A decision guide by situation: solo pre-product, an MVP build with Claude Code, ops with Cowork, or a small team. Plan names and model choice, no prices. - [The AI Engineer Roadmap for 2026](https://agentscamp.com/guides/getting-started/ai-engineer-roadmap-2026.md): A staged path from API calls to production agents — the skills that matter in 2026, what to skip, and the guides and tools for each stage, in order. - [The Best Claude Code Agents, Skills & Commands to Install First](https://agentscamp.com/guides/getting-started/best-claude-code-agents-skills.md): A curated starter kit from the AgentsCamp library — the subagents, skills, and slash commands that pay off immediately, by workflow. - [Choosing the Right Model: Haiku vs Sonnet vs Opus](https://agentscamp.com/guides/getting-started/choosing-the-right-model.md): How to pick the right Claude model tier — Haiku, Sonnet, or Opus — for any Claude Code agent or task, with a clear decision rubric and per-agent examples. - [25 Claude Code Tips, Shortcuts, and Power Features](https://agentscamp.com/guides/getting-started/claude-code-tips.md): The 25 highest-leverage Claude Code tips — keyboard shortcuts, bash and memory prefixes, session commands, model tricks, and power features most people miss. - [What Is Claude Cowork? The Complete Guide for Non-Developers](https://agentscamp.com/guides/getting-started/claude-cowork-guide.md): Claude Cowork explained for non-developers: what the desktop agent does with your files and apps, how it differs from chat and Claude Code, plans, and safety. - [Anthropic's Knowledge-Work Plugins: Every Role Plugin, Explained](https://agentscamp.com/guides/getting-started/claude-knowledge-work-plugins.md): Every Anthropic knowledge-work plugin for Claude Cowork and Claude Code cataloged: what each role plugin bundles, the install commands, and how to fork one. - [Claude Free vs Pro vs Max vs Team (2026): Which Plan Unlocks What](https://agentscamp.com/guides/getting-started/claude-plans-compared-2026.md): Every Claude plan compared as of September 2026: prices, usage tiers, and which plan unlocks Claude Code, Cowork, Design, Excel, Chrome, projects, and models. - [Getting Started with Claude Code Agents](https://agentscamp.com/guides/getting-started/getting-started-with-agents.md): What Claude Code subagents are, why they help, and how to add your first one. - [Installing Claude Code](https://agentscamp.com/guides/getting-started/installing-claude-code.md): Install Claude Code, authenticate, start a session in a real project, and add a minimal CLAUDE.md. - [OpenAI Codex: A Practical Guide for Developers](https://agentscamp.com/guides/getting-started/openai-codex-guide.md): Learn how OpenAI Codex works across the terminal, IDE, desktop app, and cloud — then set up a safe, repeatable workflow for real repositories. - [How to Update Claude Code (and Fix Update Errors)](https://agentscamp.com/guides/getting-started/update-claude-code.md): Update Claude Code for every install method: claude update vs npm install -g, auto-update channels, pinning a version, and fixes for common update errors. - [What Is Claude Code?](https://agentscamp.com/guides/getting-started/what-is-claude-code.md): A grounded explanation of Claude Code: an agentic command-line coding tool that reads files, runs commands, and works in a loop toward a goal. - [Writing Your First Custom Agent](https://agentscamp.com/guides/getting-started/writing-a-custom-agent.md): A step-by-step guide to authoring a focused, effective custom subagent. - [Test AI Asset Search with Known Asset Examples](https://agentscamp.com/guides/marketing/ai-asset-search-acceptance-test.md): Evaluate asset discovery with a small set of known examples, expected exclusions and reviewed results before relying on natural-language catalog search. - [Does AI-Written Content Rank in 2026? What Google Actually Says](https://agentscamp.com/guides/marketing/ai-content-and-search-2026.md): What Google's own documentation says about AI-generated content in 2026: what it rewards, what scaled content abuse is, how AI features cite, and a checklist. - [Review AI Digital Asset Metadata with a Controlled Vocabulary](https://agentscamp.com/guides/marketing/ai-digital-asset-metadata.md): Review AI-suggested asset tags against a supplied vocabulary, original asset versions and human visual observations before changing a catalog. - [Review AI Localization with a Glossary and Source-Target Checks](https://agentscamp.com/guides/marketing/ai-localization-review-workflow.md): Review AI-translated copy with a source-target register, approved terminology, protected placeholders, contextual checks, and qualified language review. - [Build a Brand-Voice Skill for Claude (and Upload It Anywhere)](https://agentscamp.com/guides/marketing/brand-voice-with-claude-skills.md): Turn 5-10 samples of your copy into a SKILL.md brand-voice skill, then upload it to claude.ai, install it in Claude Code, use it in Cowork, and test it. - [Claude Code for Marketers: The Complete 2026 Guide](https://agentscamp.com/guides/marketing/claude-code-for-marketers.md): Why a marketer would open a terminal agent, when claude.ai or Cowork is the better tool, a CLAUDE.md for a content repo, five workflows, and what goes wrong. - [Claude for Marketing Teams: claude.ai, Cowork, Claude Design, and Connectors](https://agentscamp.com/guides/marketing/claude-for-marketing-teams.md): A surface map for marketing teams: which Claude product to open for brand context, asset folders, recurring reports, decks, CMS chores, and connectors. - [Anthropic's Marketing Plugin for Claude: Every Skill and Command Explained](https://agentscamp.com/guides/marketing/claude-marketing-plugin-guide.md): Every skill in Anthropic's open-source marketing plugin for Claude Cowork and Claude Code, its connectors, the install commands, and how to fork it. - [Claude Skills for Marketers: 6 to Upload Today](https://agentscamp.com/guides/marketing/claude-skills-for-marketers.md): Six Claude skills for marketers (brand voice, SEO brief, repurposing, landing page, email sequence, documents), how to upload them, and a prompt for each. - [An SEO Content Workflow in Claude Code: Brief, Draft, On-Page Check](https://agentscamp.com/guides/marketing/seo-content-workflow-with-claude-code.md): A repeatable SEO content workflow in Claude Code: a content repo with CLAUDE.md, SERP research over Exa and Firecrawl MCP, a brief, a draft, and on-page checks. - [Which Claude Plan Should a Marketer Pay For?](https://agentscamp.com/guides/marketing/which-claude-plan-for-marketers.md): A decision guide by situation: solo content marketer, a small team sharing brand context, heavy Cowork and Claude Design use, or an agency. No prices. - [The Best MCP Servers in 2026](https://agentscamp.com/guides/mcp/best-mcp-servers-2026.md): The MCP servers actually worth connecting in 2026 — Context7, GitHub, Chrome DevTools, Playwright, Serena, Exa, Firecrawl, and official vendor servers. - [Adding MCP Servers to Claude Code: Local, Remote, and Project-Scoped](https://agentscamp.com/guides/mcp/claude-code-mcp-setup.md): The complete claude mcp add reference — stdio vs HTTP transports, local/project/user scopes, .mcp.json with env expansion, OAuth via /mcp, and the gotchas. - [Codex MCP Setup: Connect Tools and Live Context](https://agentscamp.com/guides/mcp/codex-mcp-setup.md): Connect OpenAI Codex to MCP servers through the CLI, desktop app, IDE, or config.toml — with practical scoping, OAuth, and tool-approval guidance. - [Deploying a Remote MCP Server: Stateless, Streamable HTTP, and Horizontal Scaling](https://agentscamp.com/guides/mcp/deploy-remote-mcp-server.md): Take an MCP server from local stdio to a remote, multi-user HTTP service — Streamable HTTP, stateless vs. stateful sessions, OAuth, and horizontal scaling. - [Connecting and Governing MCP Servers: Registries, Gateways, and Tool Sprawl](https://agentscamp.com/guides/mcp/govern-mcp-servers.md): As MCP servers multiply, discovery, trust, and tool sprawl become the problem. How registries, gateways, and curation keep a growing fleet secure and usable. - [MCP Ecosystem Statistics 2026](https://agentscamp.com/guides/mcp/mcp-ecosystem-statistics.md): The Model Context Protocol by the numbers — SDK downloads, server counts across registries, governance facts, and growth since the Linux Foundation donation. - [MCP vs A2A: AI Agent Protocols Explained](https://agentscamp.com/guides/mcp/mcp-vs-a2a.md): What MCP and A2A each standardize, how Agent Cards and Tasks work, why the two protocols are complementary, and who governs them now (both Linux Foundation). - [Deploying LLMs to Production: A Reliability & Cost Checklist](https://agentscamp.com/guides/mlops/deploying-llms-to-production.md): Take an LLM feature from prototype to production: API vs self-host, provider fallback, retries, caching, observability, eval gates, and safe rollout. - [Preparing a Fine-Tuning Dataset: Cleaning, Synthetic Data, and Eval Splits](https://agentscamp.com/guides/mlops/finetune-dataset-prep.md): The dataset is the model. How to build a fine-tuning dataset that works — format, curation, cleaning, synthetic augmentation, and a leak-free eval split. - [Fine-Tune vs RAG vs Prompt vs Distill: The 2026 Decision Tree](https://agentscamp.com/guides/mlops/finetune-vs-rag-vs-prompt.md): When to reach for prompt engineering, RAG, fine-tuning, or distillation — what each actually changes, where each fails, and how to combine them. - [LLM Observability in Production: Traces, Evals, Cost, and Feedback](https://agentscamp.com/guides/mlops/llm-observability-production.md): Instrument production LLM applications with end-to-end traces, online evals, cost and latency metrics, privacy controls, alerts, and a failure-feedback loop. - [Self-Host vs API: When Does Running Your Own LLM Actually Pay Off?](https://agentscamp.com/guides/mlops/self-host-vs-api-llm.md): The real economics of self-hosting an LLM vs. calling a hosted API — GPU utilization, privacy, latency, and the hidden ops costs that decide the crossover. - [SLOs and Error Budgets: A Practical Reliability Guide](https://agentscamp.com/guides/observability/slo-error-budget-guide.md): Define user-centered SLIs and SLOs, calculate error budgets, create burn-rate alerts, set release policy, and keep reliability targets meaningful. - [Web Performance Budgets: Turn Speed Into a Release Gate](https://agentscamp.com/guides/performance/web-performance-budgets.md): Define and enforce web performance budgets for Core Web Vitals, JavaScript, images, fonts, third parties, and critical journeys in CI and production. - [AI Coding Agents in 2026: The Open-Source & CLI Edition](https://agentscamp.com/guides/prompting/ai-coding-agents-cli-2026.md): Cursor and Windsurf vs the open-source agents — OpenCode, Cline, Aider, Codex CLI, and more. Who should bring their own model, and when to stay in the terminal. - [Claude vs GPT vs Gemini for Coding in 2026](https://agentscamp.com/guides/prompting/claude-vs-gpt-vs-gemini-coding.md): The three frontier model families compared for real coding work — agentic depth, ecosystem fit, context, and cost shape — plus how to actually choose. - [Context Engineering](https://agentscamp.com/guides/prompting/context-engineering.md): Treating the context window as a finite budget — what to load, what to leave out, and when to reset. - [Cursor vs Claude Code vs GitHub Copilot vs Windsurf in 2026](https://agentscamp.com/guides/prompting/cursor-vs-claude-code-vs-copilot-vs-windsurf-2026.md): A practical, opinionated comparison of the four mainstream AI coding tools — form factor, agentic depth, model choice, and who each one is for. - [Designing System Prompts for LLM Apps and Agents](https://agentscamp.com/guides/prompting/designing-system-prompts.md): How to write system prompts that hold up in production: what belongs there vs. the user turn, structure that survives long context, and format/refusal rules. - [Programmatic Prompt Optimization with DSPy: Stop Hand-Tuning Prompts](https://agentscamp.com/guides/prompting/dspy-prompt-optimization.md): Hand-tuning prompts doesn't scale. DSPy treats prompting as programming — declare tasks as typed signatures and let an optimizer compile the prompts for you. - [Effective Tool Use: Scoping an Agent's Toolset](https://agentscamp.com/guides/prompting/effective-tool-use.md): How to scope tools and permissions so an agent reaches for the right one and can't do damage. - [Prompt Patterns for Coding Agents](https://agentscamp.com/guides/prompting/prompt-patterns.md): Practical prompting patterns: chaining, few-shot, context management, tool use, and output structuring. - [Few-Shot vs Chain-of-Thought vs Structured Prompting: What to Use When (2026)](https://agentscamp.com/guides/prompting/prompting-techniques-2026.md): When to reach for few-shot examples, chain-of-thought reasoning, or structured/output-constrained prompting — a 2026 decision guide to the core techniques. - [Vibe Coding in 2026: What It Is, When It Works, When It Bites](https://agentscamp.com/guides/prompting/vibe-coding-guide.md): An honest guide to vibe coding — where prompt-and-accept development genuinely pays, where it accumulates risk, and the guardrails that make it professional. - [Build an RFP Compliance Matrix with AI and Evidence](https://agentscamp.com/guides/sales/ai-rfp-compliance-matrix.md): Build an RFP compliance matrix with AI that maps every requirement to source text, approved evidence, response locations, owners, and unresolved gaps. - [Claude Code for Revenue Ops: Connectors, CRM Audits, and the Fine Print](https://agentscamp.com/guides/sales/claude-code-for-revenue-ops.md): Running Anthropic's sales plugin in Claude Code: wiring MCP connectors, auditing a CRM export, and the vendor terms that decide what you may connect. - [Claude for Sales Teams: The Surface Map](https://agentscamp.com/guides/sales/claude-for-sales-teams.md): Which Claude surface a sales team should actually open: Anthropic's free sales plugin, claude.ai Projects, Cowork, Claude Code, and MCP connectors. - [Inside Anthropic's Sales Plugin for Claude: All Nine Skills](https://agentscamp.com/guides/sales/claude-sales-plugin-guide.md): A per-skill breakdown of Anthropic's sales plugin for Claude: what each of the nine skills produces, how you trigger it, and where it falls down. - [Claude Skills for Sales: The Set Worth Installing](https://agentscamp.com/guides/sales/claude-skills-for-sales.md): The Claude skills a sales team should install: Anthropic's nine official ones, plus four of ours that verify and audit what the official set writes. - [How to Research a Prospect with Claude (and Verify It Before You Send)](https://agentscamp.com/guides/sales/research-prospects-with-claude.md): A step-by-step prospect research workflow in Claude: run account-research standalone, add an enrichment connector, draft the email, then verify every claim. - [Which Claude Plan Should a Sales Team Pay For?](https://agentscamp.com/guides/sales/which-claude-plan-for-sales-teams.md): Which Claude surface a sales team needs — Cowork for the plugin, Projects for account context, connectors, Claude Code for ops — and where the numbers live. - [The Agent Skills Standard: One SKILL.md for Every AI Tool](https://agentscamp.com/guides/skills/agent-skills-open-standard.md): Agent Skills became an open standard in December 2025. Which tools read SKILL.md today — Copilot, Cursor, VS Code, Gemini CLI, Codex — and how to write portable skills. - [The Best Claude Skills to Install in 2026](https://agentscamp.com/guides/skills/best-claude-skills-2026.md): A skills-only tour of the AgentsCamp library — the Claude Code skills that earn a permanent slot, organized by the job they do. - [10 Best Claude Skills for Building AI Applications](https://agentscamp.com/guides/skills/best-claude-skills-for-ai-apps.md): Compare Claude skills for structured output, fallbacks, evals, hallucinations, routing, token cost, semantic caching, PII, guardrails, and tools. - [10 Best Claude Skills for API Development](https://agentscamp.com/guides/skills/best-claude-skills-for-api-development.md): Choose Claude skills for error contracts, idempotency, pagination, limits, webhooks, CORS, GraphQL, OpenAPI, tools, and MCP servers. - [9 Best Claude Skills for Code Review](https://agentscamp.com/guides/skills/best-claude-skills-for-code-review.md): Choose Claude skills for security, correctness, maintainability, and focused pull request review without relying on one vague review prompt. - [8 Best Claude Skills for Database Work](https://agentscamp.com/guides/skills/best-claude-skills-for-database-work.md): Compare Claude skills for safe migrations, data backfills, indexes, query plans, pooling, deadlocks, vector search, and SQL tuning. - [10 Best Claude Skills for Debugging](https://agentscamp.com/guides/skills/best-claude-skills-for-debugging.md): Compare Claude skills and commands for stack traces, failing and flaky tests, regressions, slow queries, memory leaks, CPU hotspots, deadlocks, and agents. - [10 Best Claude Skills for DevOps and SRE](https://agentscamp.com/guides/skills/best-claude-skills-for-devops-sre.md): Use Claude skills for incidents, rollbacks, SLOs, alerts, dashboards, logs, traces, containers, CI, and safer releases. - [7 Best Claude Skills for Documentation](https://agentscamp.com/guides/skills/best-claude-skills-for-documentation.md): Use Claude skills to verify code examples and create accurate READMEs, API docs, runbooks, onboarding guides, ADRs, and diagrams. - [9 Best Claude Skills for Frontend Development](https://agentscamp.com/guides/skills/best-claude-skills-for-frontend-development.md): Compare Claude skills for accessibility, Web Vitals, React renders, bundles, localization, browser security, test data, tests, and caching. - [9 Best Claude Skills for Git](https://agentscamp.com/guides/skills/best-claude-skills-for-git.md): Compare Claude skills and commands for commit messages, undoing mistakes, PR descriptions, rebases, conflicts, commit splitting, blame, and changelogs. - [9 Best Claude Skills for Performance Engineering](https://agentscamp.com/guides/skills/best-claude-skills-for-performance.md): Compare Claude skills for caching, Web Vitals, bundles, React renders, load tests, cold starts, CPU profiles, memory leaks, and prompt caches. - [10 Best Claude Skills for RAG](https://agentscamp.com/guides/skills/best-claude-skills-for-rag.md): Compare Claude skills and commands for RAG scaffolding, chunking, grounding checks, embeddings, reranking, pgvector, index tuning, and GraphRAG. - [10 Best Claude Skills for Refactoring](https://agentscamp.com/guides/skills/best-claude-skills-for-refactoring.md): Compare Claude skills and commands for clean-code passes, renames, module splits, dead code, upgrades, import cycles, flags, strict types, and migrations. - [9 Best Claude Skills for Release Management](https://agentscamp.com/guides/skills/best-claude-skills-for-release-management.md): Use Claude skills for readiness reviews, canaries, rollbacks, API deprecations, migrations, versioning, changelogs, and release notes. - [10 Best Claude Skills for Security Work](https://agentscamp.com/guides/skills/best-claude-skills-for-security.md): Compare Claude skills for secrets, dependencies, permissions, auth, RBAC, threats, headers, PII, data retention, and LLM guardrails. - [9 Best Claude Skills for Software Testing](https://agentscamp.com/guides/skills/best-claude-skills-for-testing.md): Compare Claude skills for regression, unit, integration, contract, property, mutation, prompt, and test-data workflows. - [Claude Code Skills: Best Practices](https://agentscamp.com/guides/skills/claude-code-skills-best-practices.md): The patterns that make Claude Code skills reliable: trigger-first descriptions, one job per skill, lean bodies, bundled scripts, and scoped tools. - [Claude's Document Skills: Excel, PowerPoint, Word, and PDF](https://agentscamp.com/guides/skills/claude-document-skills.md): How Anthropic's pre-built document skills let Claude produce real .xlsx, .pptx, .docx, and PDF files — on claude.ai, the API, and in Claude Code. - [Claude Skills Examples: Annotated SKILL.md Files](https://agentscamp.com/guides/skills/claude-skills-examples.md): Real SKILL.md examples you can copy — a minimal skill, a scoped-tools skill, a bundled-script skill — with the reasoning behind each line. - [Claude Skills on claude.ai and the API](https://agentscamp.com/guides/skills/claude-skills-on-claude-ai-and-api.md): How Agent Skills work beyond Claude Code: uploading to claude.ai, the /v1/skills API with code execution, Managed Agents, and the Agent SDK. - [Claude Skills Use Cases: 20 Ideas Worth Building](https://agentscamp.com/guides/skills/claude-skills-use-cases.md): Twenty concrete Claude skills use cases — for engineers, writers, analysts, and ops — with the pattern behind each and links to installable versions. - [Codex Skills: Build Reusable Workflows with SKILL.md](https://agentscamp.com/guides/skills/codex-skills-guide.md): Create, install, and test Codex skills with SKILL.md — including trigger descriptions, repo and user paths, progressive disclosure, and distribution. - [How to Install Claude Skills](https://agentscamp.com/guides/skills/how-to-install-claude-skills.md): Every way to install Claude skills: manual copy, the agentscamp CLI, GitHub repos, plugins, team distribution, and uploading to claude.ai. - [Packaging and Sharing Claude Code Skills](https://agentscamp.com/guides/skills/packaging-and-sharing-skills.md): Take a skill from your personal ~/.claude folder to a versioned plugin your whole team installs from a marketplace — portably and with governance. - [The SKILL.md Reference: Every Frontmatter Field Explained](https://agentscamp.com/guides/skills/skill-md-reference.md): A complete reference for the SKILL.md format — all frontmatter fields, naming rules, argument substitution, limits, and where skill files live. - [Skills vs Agents vs Commands](https://agentscamp.com/guides/skills/skills-vs-agents-vs-commands.md): How Claude Code's two extension mechanisms — subagents and skills — differ across three invocation patterns, with a decision table for choosing the right one. - [Skills vs MCP Servers: When to Use Which](https://agentscamp.com/guides/skills/skills-vs-mcp-servers.md): Skills inject procedure into context; MCP servers expose tools and live data over a protocol. A decision framework, the combine pattern, and examples. - [Testing and Debugging Claude Code Skills](https://agentscamp.com/guides/skills/testing-and-debugging-skills.md): Verify a Claude Code skill triggers on the right prompts, check its output, and fix the five common failures — from vague triggers to broken paths. - [What Are Claude Skills? The Complete Guide](https://agentscamp.com/guides/skills/what-are-claude-skills.md): Claude Skills explained: what a SKILL.md is, how progressive disclosure keeps skills cheap, where they run, and how to install or write your own. - [Writing Your First Skill](https://agentscamp.com/guides/skills/writing-your-first-skill.md): A step-by-step guide to packaging a reusable procedure as a Claude Code skill that loads exactly when it's needed. - [Contract Testing for Microservices: Consumer, Provider, and CI](https://agentscamp.com/guides/testing/contract-testing-microservices.md): Catch breaking service changes before deployment with consumer-driven contracts, provider verification, state fixtures, compatibility gates, and schema tests. - [TDD with AI Agents: Red-Green as an Agent Loop](https://agentscamp.com/guides/testing/tdd-with-ai-agents.md): Test-driven development found its killer app: agents. How write-the-test-first turns AI coding into a verifiable loop, and the workflow that makes it stick. - [How to Test AI-Generated Code](https://agentscamp.com/guides/testing/testing-ai-generated-code.md): AI writes the code; tests decide whether to trust it. The verification stack for agent-written changes — contracts, generated tests, and the review that's left. - [Testing LLM Applications: How to Test Non-Deterministic Software](https://agentscamp.com/guides/testing/testing-llm-applications.md): How to test software that calls LLMs when outputs are non-deterministic — the testing pyramid, assertion strategies, golden datasets, and CI gating. - [Claude Code Troubleshooting: Fixes for the Most Common Problems](https://agentscamp.com/guides/troubleshooting/claude-code-troubleshooting.md): Fixes for the Claude Code problems people actually hit — install and auth failures, context-limit errors, MCP servers that won't connect, permission loops. - [Claude Skills Not Working? Fixes for Every Failure Mode](https://agentscamp.com/guides/troubleshooting/claude-skills-not-working.md): Skill missing from the / menu, never auto-triggering, firing too often, or breaking mid-run — the symptom-by-symptom fix list for Claude skills. - [Codex Troubleshooting: A Layer-by-Layer Recovery Guide](https://agentscamp.com/guides/troubleshooting/codex-troubleshooting.md): Troubleshoot OpenAI Codex when files, commands, configuration, worktrees, MCP, or app features misbehave — with a fast isolation and recovery sequence. - [Why Your Agent Loops: Debugging AI Agents](https://agentscamp.com/guides/troubleshooting/debugging-ai-agents.md): The recurring agent failure modes — loops, premature victory, tool misuse, context poisoning, scope creep — diagnosed by their signatures, with fixes. - [MCP Troubleshooting: Server Won't Connect & Other Fixes](https://agentscamp.com/guides/troubleshooting/mcp-troubleshooting.md): Fixes for the MCP problems people actually hit — servers failing to connect, missing tools, OAuth loops, timeouts, truncated output, and Windows quirks. - [Why RAG Fails: A Debugging Checklist](https://agentscamp.com/guides/troubleshooting/rag-debugging-checklist.md): A diagnostic checklist for broken RAG — localize the failure to ingestion, retrieval, ranking, or generation, and apply the fix that matches, in order. - [Add Image Understanding to Your App](https://agentscamp.com/guides/vision/add-image-understanding-to-your-app.md): A practical guide to sending images to a vision model and getting reliable, structured results: base64 vs URL, resolution, prompting, cost, and errors. - [Multimodal Embeddings and Image Search](https://agentscamp.com/guides/vision/multimodal-embeddings-and-image-search.md): How multimodal embeddings put images and text in one vector space, and how to build text-to-image and image-to-image search on top of it. - [Multimodal RAG over PDFs, Scans & Charts: Two Approaches That Actually Work](https://agentscamp.com/guides/vision/multimodal-rag-images-pdfs.md): RAG over visual documents — PDFs, scans, charts — where text-only extraction loses tables and layout. Parse-then-text vs embed-the-page-image, with trade-offs. - [Review OCR Text Against the Original Scan](https://agentscamp.com/guides/vision/review-ocr-text-against-scans.md): Review OCR text beside source scans, preserve exact wording and document uncertain characters in a correction ledger before producing a corrected copy. - [Screenshot-to-Code: Building UIs from Images with AI](https://agentscamp.com/guides/vision/screenshot-to-code-with-ai.md): Turn a screenshot, mockup, or Figma frame into working frontend code with AI vision models — the realistic workflow, the right tools, and the honest pitfalls. - [Vision-Language Models Compared (2026)](https://agentscamp.com/guides/vision/vision-language-models-compared-2026.md): Which vision-language model to reach for, by job: Claude, GPT, Gemini, and open models like Qwen3-VL compared on OCR, charts, grounding, video, and cost. - [Using Vision-Language Models for OCR, Documents, and Video Understanding](https://agentscamp.com/guides/vision/vlm-ocr-documents.md): How to use vision-language models for OCR, documents, and video: how they differ from traditional OCR, their failure modes, and getting reliable output. - [Best Speech-to-Text APIs in 2026](https://agentscamp.com/guides/voice/best-stt-apis-2026.md): The STT field, honestly ranked — Deepgram and AssemblyAI's hosted duel, Whisper as the open baseline, Cartesia Ink for latency — and how to pick by workload. - [Best Text-to-Speech APIs in 2026](https://agentscamp.com/guides/voice/best-tts-apis-2026.md): The TTS APIs worth building on — ElevenLabs for quality and breadth, Cartesia Sonic for realtime latency — and how to choose for agents vs produced audio. - [How to Build a Voice Agent: The STT → LLM → TTS Pipeline](https://agentscamp.com/guides/voice/build-a-voice-agent.md): How to build a real-time voice agent: the STT → LLM → TTS pipeline, the latency budget that makes or breaks it, and how to wire each stage. - [Realtime Voice Agents: Build on LiveKit, Buy Vapi, or Pipeline with Pipecat](https://agentscamp.com/guides/voice/realtime-voice-apis.md): The three ways to ship a realtime voice agent in 2026 — open infrastructure, managed platform, or OSS pipeline — and how speech-to-speech models fit in. - [Review AI Captions Before Publishing](https://agentscamp.com/guides/voice/review-ai-captions-before-publishing.md): Review AI-generated captions against recorded speech, speaker changes and meaningful sounds, then check a separate timing file before publication. - [Review AI Speech Cleanup Against the Original](https://agentscamp.com/guides/voice/review-ai-speech-cleanup.md): Compare an AI-cleaned voice recording with the original using timed listening notes, intelligibility checks and a clear keep, retry or rerecord decision. - [An AI Code Review Workflow That Actually Catches Bugs](https://agentscamp.com/guides/workflow/ai-code-review-workflow.md): Layer the review stack — self-review, AI reviewers, tests, and a human pass focused on what machines miss — into a workflow tuned for AI-written code. - [Build an AI Customer Support Answer Library You Can Check](https://agentscamp.com/guides/workflow/ai-customer-support-answer-library.md): Build an AI support answer library with source-backed answer cards, eligibility limits, escalation rules, and checked customer-question test cases. - [How to Research Documents with AI and Verify Citations](https://agentscamp.com/guides/workflow/ai-document-research-with-citations.md): Turn a set of documents into a defensible AI research brief with a source register, conflict table, citation checks, and clear unanswered questions. - [How to Summarize an Email Thread with AI and Draft a Reply](https://agentscamp.com/guides/workflow/ai-email-thread-summary-reply.md): Use AI to reconstruct a long email thread, distinguish current decisions from old requests, identify open questions, and draft a reply for review. - [Draft AI Exit Tickets from Lesson Objectives](https://agentscamp.com/guides/workflow/ai-exit-tickets-from-lesson-objectives.md): Draft short AI-assisted exit tickets from taught material, map each prompt to a learning objective, and review the answer key before classroom use. - [AI Literature Review Workflow: Screen, Extract, and Verify](https://agentscamp.com/guides/workflow/ai-literature-review-workflow.md): Use AI for a literature review with explicit eligibility criteria, study-level evidence tables, source locations, and checks for missing or conflicting data. - [AI Meeting Notes: Turn Transcripts into Decisions and Action Items](https://agentscamp.com/guides/workflow/ai-meeting-notes-action-items.md): Use AI to turn a meeting transcript into clear minutes, with evidence for decisions, confirmed owners, unresolved questions, and a reviewable action list. - [Create an AI Presentation from a Report: A Reviewable Workflow](https://agentscamp.com/guides/workflow/ai-presentation-from-report.md): Turn a business report into an AI-assisted presentation with a claim-to-slide map, source checks, speaker notes, and a finished-deck review. - [Create an AI Project Handoff Another Teammate Can Use](https://agentscamp.com/guides/workflow/ai-project-handoff.md): Create a project handoff with AI that preserves source files, current decisions, open questions, owners, and an acceptance check for the next teammate. - [Create Standard Operating Procedures with AI from a Real Workflow](https://agentscamp.com/guides/workflow/ai-standard-operating-procedures.md): Create an SOP with AI from captured steps, explicit prerequisites, exception paths, and a teammate walkthrough that checks the procedure. - [Build AI Study Cards from Your Notes](https://agentscamp.com/guides/workflow/ai-study-cards-from-notes.md): Turn approved notes into checked AI study cards with one learning target, source quotes, unresolved questions and a learner review before practice. - [Plan AI Survey Branching Before Building the Form](https://agentscamp.com/guides/workflow/ai-survey-branching-plan.md): Turn approved survey questions and routing rules into an explicit branch table, with stable IDs and unresolved paths before an AI form builder runs. - [Prepare a Vendor Contract Review Packet with AI](https://agentscamp.com/guides/workflow/ai-vendor-contract-review-packet.md): Prepare a vendor contract review packet with AI that preserves clause evidence, conditions, obligation dates, missing documents, and reviewer questions. - [Analyze Customer Feedback with AI: Themes You Can Check](https://agentscamp.com/guides/workflow/analyze-customer-feedback-with-ai.md): Analyze customer feedback with AI using a source register, a reviewed codebook, distinct customer counts, and examples that preserve context. - [Codex Automations: Schedule Reliable Background Work](https://agentscamp.com/guides/workflow/codex-automations.md): Schedule Codex tasks for recurring checks and follow-ups — choose chat or standalone runs, local projects or worktrees, skills, permissions, and stopping rules. - [Human-in-the-Loop AI Workflows: Approval Gates That Keep Agents Safe and Trusted](https://agentscamp.com/guides/workflow/human-in-the-loop-ai-workflows.md): How to design human-in-the-loop into agent workflows — when to require approval, gate patterns, confidence escalation, review UX, and feedback loops. - [Build a Source-Grounded AI Mind Map](https://agentscamp.com/guides/workflow/source-grounded-ai-mind-map.md): Turn a bounded document set into a reviewed concept hierarchy with evidence for each branch and visible unresolved relationships. - [Spec-Driven Development with AI Agents](https://agentscamp.com/guides/workflow/spec-driven-development.md): Write the spec, let the agent implement against it — the SDD workflow (spec → plan → tasks → implement), when it beats prompt-and-iterate, and the tooling. - [Test AI Survey Logic with Known Response Paths](https://agentscamp.com/guides/workflow/test-ai-survey-logic.md): Create complete response-path cases for an AI-built survey, check a local route model, then preview the actual form before collection. ## Tools A curated directory of AI coding tools, editors, agents, and MCP servers. - [ABBYY FineReader PDF](https://agentscamp.com/tools/abbyy-finereader-pdf.md): ABBYY FineReader PDF applies AI-based OCR to scans and PDFs, with a Windows OCR Editor for checking recognized text against the source image. - [Adobe Podcast](https://agentscamp.com/tools/adobe-podcast.md): Adobe Podcast offers browser recording and editing plus AI Enhance Speech for reducing noise in voice recordings, with free and premium access. - [AgentOps](https://agentscamp.com/tools/agentops.md): Observability for AI agents — session replay, cost and latency tracking, and debugging for multi-step runs. - [Agno](https://agentscamp.com/tools/agno.md): A Python framework for building multi-agent systems with memory, knowledge, and tools — formerly Phidata, now with the AgentOS runtime. - [Aider](https://agentscamp.com/tools/aider.md): Open-source terminal AI pair programmer that edits files in your Git repo and auto-commits each change, working with Claude, GPT, and other models you bring. - [Alttext AI](https://agentscamp.com/tools/alttext-ai.md): Evaluate AltText.ai drafts against inspected observations, page purpose, correction notes and the actual publishing review process. - [Amp](https://agentscamp.com/tools/amp.md): Agentic coding tool, independent of Sourcegraph since December 2025: a CLI plus apps for the web, Mac, iPhone, and iPad, tuned for frontier-model coding. - [Google Antigravity](https://agentscamp.com/tools/antigravity.md): Google's agentic development platform — an agent-first IDE and Manager surface where multiple agents work across editor, terminal, and browser, on Gemini 3. - [Apollo](https://agentscamp.com/tools/apollo.md): Apollo pairs a 240M+ contact database with outbound sequencing in one seat, and its MCP server is the most agentic thing about it. - [Argilla](https://agentscamp.com/tools/argilla.md): Evaluate Argilla for a supervised text task with separately preserved suggestions, human responses and decision evidence. - [Arize Phoenix](https://agentscamp.com/tools/arize-phoenix.md): A source-available (Elastic License 2.0) LLM observability and evaluation tool built on OpenTelemetry, runnable anywhere. - [Assemblyai](https://agentscamp.com/tools/assemblyai.md): Speech AI platform: promptable Universal-3 Pro STT, a flat-rate Voice Agent API, and speech understanding — summarization, sentiment, PII redaction. - [Attio](https://agentscamp.com/tools/attio.md): Attio is a developer-extensible CRM with a user-defined data model, agents that research and act on records, and a live MCP endpoint for Claude. - [Augment Code](https://agentscamp.com/tools/augment-code.md): AI coding assistant built for large, real-world codebases — a Context Engine that indexes the whole repo, with agents and chat in IDEs and a CLI. - [AutoGen (AG2)](https://agentscamp.com/tools/autogen.md): A multi-agent conversation framework where agents collaborate via message-passing, with group chat and code execution. - [BAML](https://agentscamp.com/tools/baml.md): A domain-specific language for type-safe LLM functions, with generated clients and schema-aligned parsing. - [Base44](https://agentscamp.com/tools/base44.md): A Wix-owned AI app builder that turns a plain-English prompt into a working web app with database, auth, and hosting built in. - [Baseten](https://agentscamp.com/tools/baseten.md): Production inference platform for ML and LLM models — autoscaling GPU deployments, scale-to-zero, and packaging via the open-source Truss framework. - [Be My Eyes](https://agentscamp.com/tools/be-my-eyes.md): Evaluate Be My Eyes for a personal picture question, recording useful clarification, remaining uncertainty and independent evidence for consequential details. - [Bolt](https://agentscamp.com/tools/bolt.md): StackBlitz's in-browser AI agent that builds, runs, and deploys full-stack web apps in a WebContainer. - [Boords](https://agentscamp.com/tools/boords.md): Boords uses AI to turn scripts into storyboard structure and image prompts, with frame notes and timed animatic previews for production planning. - [Braintrust](https://agentscamp.com/tools/braintrust.md): An end-to-end platform for evaluating, iterating on, and observing LLM apps, with a prompt playground. - [Brave Search API](https://agentscamp.com/tools/brave-search-api.md): Brave's web search API on its own independent index: ranked results, LLM Context grounding chunks, and cited answers from an OpenAI-compatible endpoint. - [Browser Use](https://agentscamp.com/tools/browser-use.md): The most-adopted open-source browser-agent framework — point an LLM at a task and it drives a real browser: navigating, clicking, typing, extracting. - [Browserbase](https://agentscamp.com/tools/browserbase.md): Managed headless-browser infrastructure for AI agents and web automation — serverless cloud browsers with stealth, proxies, live view, and Playwright/Stagehand. - [Bubble](https://agentscamp.com/tools/bubble.md): A full no-code platform for web and native mobile apps with its own database, visual workflow logic, and AI generation from a prompt. - [Bynder](https://agentscamp.com/tools/bynder.md): Bynder centralizes digital assets and offers AI-assisted metadata and discovery, including image search and similarity-based asset finding. - [Canva](https://agentscamp.com/tools/canva.md): A design platform whose Magic Studio and Canva AI features generate designs, copy, and edits on top of your brand kit, across web, desktop, and mobile. - [Cartesia](https://agentscamp.com/tools/cartesia.md): Real-time voice AI on state-space models — Sonic streaming TTS, Ink STT with native turn detection, and Line, a code-first voice-agent platform. - [Cerebras](https://agentscamp.com/tools/cerebras.md): Cerebras Inference serves open-weight LLMs on wafer-scale chips for very fast output, through an OpenAI-compatible API with a free trial credit. - [Chatgpt](https://agentscamp.com/tools/chatgpt.md): OpenAI's general-purpose AI assistant for chat, research, and finished documents, with Projects, memory, plugins, and a Work mode for multi-step tasks. - [Chonkie](https://agentscamp.com/tools/chonkie.md): A lightweight, fast chunking library for RAG with many splitting strategies in one API. - [Chroma](https://agentscamp.com/tools/chroma.md): An open-source, Python-first vector database that runs in-process — the fastest path from pip install to a working retrieval prototype. - [Chrome DevTools MCP](https://agentscamp.com/tools/chrome-devtools-mcp.md): Google's official MCP server that gives coding agents a live Chrome — Puppeteer automation plus DevTools network, console, and performance insights. - [Claude Agent SDK](https://agentscamp.com/tools/claude-agent-sdk.md): A toolkit for building custom agents on the same harness that powers Claude Code. - [Claude Code](https://agentscamp.com/tools/claude-code.md): Anthropic’s official agentic coding tool that runs in the terminal, IDE, and web. - [Claude Context](https://agentscamp.com/tools/claude-context.md): Zilliz's MIT-licensed MCP server that indexes your codebase into Milvus or Zilliz Cloud and gives your agent hybrid semantic code search. - [Claude Cowork](https://agentscamp.com/tools/claude-cowork.md): Anthropic's agent for knowledge work that completes multi-step tasks in your folders, connectors, and browser, on desktop, web, and mobile. - [Claude Design](https://agentscamp.com/tools/claude-design.md): An Anthropic Labs tool for making prototypes, decks, and one-pagers with Claude, using your design system, with PPTX, PDF, HTML, and Canva export. - [Claude For Chrome](https://agentscamp.com/tools/claude-for-chrome.md): Anthropic's Chrome extension that lets Claude read pages, click, fill forms, and run tasks in your signed-in browser, with site permissions and action checks. - [Claude For Excel](https://agentscamp.com/tools/claude-for-excel.md): Anthropic's Excel add-in that explains, edits, debugs, and builds spreadsheet models in place, with cell-level citations, on paid Claude plans. - [Claude](https://agentscamp.com/tools/claude.md): Anthropic's AI assistant app for web, desktop, and mobile, with Projects, connectors, Artifacts, skills, memory, and code execution. - [Clay](https://agentscamp.com/tools/clay.md): Clay is a GTM data workbench where every table column runs an enrichment step, with Claygent research agents and a Claude connector listing seven tools. - [Clearscope](https://agentscamp.com/tools/clearscope.md): A content optimization platform that grades drafts against top-ranking pages, discovers queries, tracks AI-answer mentions, and monitors published pages. - [Cline](https://agentscamp.com/tools/cline.md): Open-source autonomous coding agent for VS Code that plans, edits files, and runs commands with diff approval, using any model you bring or a local runtime. - [Close](https://agentscamp.com/tools/close.md): Close is an all-in-one CRM for high-velocity inside sales that bundles calling, SMS and email into the record, plus Chloe, an autonomous voice agent. - [Cloudflare MCP](https://agentscamp.com/tools/cloudflare-mcp.md): Cloudflare's official MCP servers — a Code Mode server covering 2,500 API endpoints in ~1k tokens, plus hosted servers for docs, Workers, and observability. - [Coderabbit](https://agentscamp.com/tools/coderabbit.md): An AI code reviewer that posts line-by-line feedback and summaries on every pull request. - [Codex CLI](https://agentscamp.com/tools/codex-cli.md): OpenAI's open-source terminal coding agent with sandboxed execution and two-layer approval controls. - [Cody](https://agentscamp.com/tools/cody.md): Sourcegraph's AI coding assistant for the IDE, grounded in deep codebase context. - [Cohere Rerank](https://agentscamp.com/tools/cohere-rerank.md): A hosted reranking API that reorders retrieved passages by true relevance to a query. - [Context7](https://agentscamp.com/tools/context7.md): Upstash's MCP server that pulls up-to-date, version-specific library documentation into your agent's context — the cure for hallucinated APIs. - [Continue](https://agentscamp.com/tools/continue.md): An open-source IDE extension for building custom AI coding assistants. - [Copy AI](https://agentscamp.com/tools/copy-ai.md): A go-to-market AI platform that codifies sales and marketing processes as Workflows, Agents, and Tables on top of a Brand Voice and Infobase. - [CrewAI](https://agentscamp.com/tools/crewai.md): A Python framework for orchestrating role-playing AI agents as collaborating 'crews', plus event-driven flows. - [Crush](https://agentscamp.com/tools/crush.md): Charm's terminal coding agent: nearly any model, including local ones, switched mid-session, with LSP context, MCP and Agent Skills. Source-available (FSL). - [Cursor](https://agentscamp.com/tools/cursor.md): An AI-first code editor built on VS Code with deep in-editor agent features, parallel agents, in-house Composer models, and a plugin marketplace. - [Databricks Genie](https://agentscamp.com/tools/databricks-genie.md): Databricks' conversational analytics layer: Genie Agents answer natural-language questions over Unity Catalog data using curated instructions and SQL. - [Daytona](https://agentscamp.com/tools/daytona.md): Sub-90ms agent sandboxes — isolated computers with snapshots, volumes, Git and LSP tools, on Linux, Windows, or Android; AGPL self-host or managed cloud. - [Dedupe](https://agentscamp.com/tools/dedupe.md): Evaluate Dedupe through an owner-reviewed matching pilot with known pairs, preserved source keys, candidate coverage checks and unresolved cases. - [DeepEval](https://agentscamp.com/tools/deepeval.md): An open-source evaluation framework for LLM apps — 'Pytest for LLMs' with ready-made metrics and CI integration. - [Deepgram](https://agentscamp.com/tools/deepgram.md): A voice-AI platform with fast, accurate speech-to-text (Nova) and low-latency text-to-speech (Aura), plus a bundled Voice Agent API. - [Deepl](https://agentscamp.com/tools/deepl.md): DeepL provides AI translation for text and documents, with preferred-term glossaries and paid options for team translation and customization workflows. - [Deepnote](https://agentscamp.com/tools/deepnote.md): A collaborative data notebook whose AI agent edits and runs your blocks, with data apps, a semantic layer, and integrations across warehouses and BI tools. - [DeepWiki MCP](https://agentscamp.com/tools/deepwiki-mcp.md): Cognition's free, no-auth remote MCP server for DeepWiki: generated wikis and grounded Q&A for any public GitHub repository, from inside your agent. - [Descript](https://agentscamp.com/tools/descript.md): A video and podcast editor where you edit by editing the transcript, with Underlord, an AI co-editor, Studio Sound, AI voices, dubbing, and clip creation. - [Desktop Commander](https://agentscamp.com/tools/desktop-commander.md): MIT-licensed MCP server that gives Claude Desktop and other MCP clients terminal control, file system search, and diff-based file editing. - [Devin](https://agentscamp.com/tools/devin.md): Cognition's autonomous AI software engineer that works in its own cloud workspace with an editor, terminal, and browser. - [Dify](https://agentscamp.com/tools/dify.md): The visual platform for LLM apps and agentic workflows — canvas-built chatflows, RAG pipeline, agent nodes with 50+ tools, and LLMOps, self-hosted via Docker. - [Docling](https://agentscamp.com/tools/docling.md): Open-source Python library that parses PDFs, DOCX, PPTX, HTML, and images into structured Markdown and JSON with layout, tables, and reading order for RAG. - [Dovetail](https://agentscamp.com/tools/dovetail.md): Dovetail organizes customer research and feedback with AI transcription, project summaries, searchable evidence, and channels for recurring signals. - [DSPy](https://agentscamp.com/tools/dspy.md): Program language models instead of prompting them: declare tasks as typed signatures and let optimizers compile the prompts and few-shot examples for you. - [E2b](https://agentscamp.com/tools/e2b.md): Open-source Firecracker-microVM sandboxes where AI agents safely run untrusted code — stateful interpreters, full Linux, pause/resume, desktop VMs. - [ElevenLabs](https://agentscamp.com/tools/elevenlabs.md): A voice-AI platform for high-quality text-to-speech, voice cloning, dubbing, and real-time conversational agents, via API. - [Elicit](https://agentscamp.com/tools/elicit.md): Elicit supports academic research with a Research Agent, research reports, and a structured workflow for screening papers and extracting evidence. - [Emergent](https://agentscamp.com/tools/emergent.md): An agentic AI app builder that plans, codes, tests, and deploys full-stack web apps and Expo mobile apps from a chat prompt. - [Exa](https://agentscamp.com/tools/exa.md): The search engine built for AIs — semantic web search, page contents, Websets, and research APIs, plus a hosted MCP server. - [Factory](https://agentscamp.com/tools/factory.md): Factory is an agent-native software development platform whose Droids plan, write, test, and ship code from the terminal, IDE, and web with org context. - [fal](https://agentscamp.com/tools/fal.md): fal is a generative-media inference cloud for running image, video, and audio diffusion models fast — 1,000+ models, a simple API, and pay-per-use pricing. - [FastMCP](https://agentscamp.com/tools/fastmcp.md): A Pythonic framework for building Model Context Protocol servers and clients — decorator-based tools, resources, and prompts, with auth and deployment built in. - [Figma Make](https://agentscamp.com/tools/figma-make.md): Figma's prompt-to-app tool: describe an idea or paste a design and get a working, code-backed prototype you can keep editing inside Figma. - [Figma MCP](https://agentscamp.com/tools/figma-mcp.md): Figma's official MCP server — structured design context, variables, screenshots, and Code Connect mappings for agents, plus write-back to the canvas. - [Firecrawl](https://agentscamp.com/tools/firecrawl.md): The API to search, scrape, and crawl the web for AI — clean Markdown out of any site, LLM-powered extraction, and a first-class MCP server. - [Fireflies](https://agentscamp.com/tools/fireflies.md): Fireflies joins Zoom, Meet and Teams calls, transcribes them, and turns transcripts into searchable notes, action items and CRM records for sales teams. - [Fireworks AI](https://agentscamp.com/tools/fireworks-ai.md): Fast production inference for open models — serverless and dedicated GPU deployments, fine-tuning, and an OpenAI-compatible API on a proprietary serving stack. - [Flowise](https://agentscamp.com/tools/flowise.md): Open-source, low-code visual builder for LLM apps and AI agents — drag-and-drop to assemble chains, agents, and RAG; self-host or use Flowise Cloud. - [Framer AI](https://agentscamp.com/tools/framer-ai.md): A website design tool with AI agents that generate editable pages, wireframes, CMS content, and code components, then publish to hosting Framer runs. - [Gamma](https://agentscamp.com/tools/gamma.md): An AI design partner that turns a prompt or a document into presentations, docs, websites, and social posts, with an Agent for edits and a credits-based plan. - [Gemini CLI](https://agentscamp.com/tools/gemini-cli.md): Google's open-source terminal AI agent powered by Gemini models, with a 1M-token context window and built-in tools. - [Gemini](https://agentscamp.com/tools/gemini.md): Google's Gemini app, a general-purpose AI assistant with Deep Research, Gems, Canvas, and direct integration into Gmail, Docs, Sheets, and Workspace. - [Github Copilot](https://agentscamp.com/tools/github-copilot.md): GitHub’s AI pair programmer with inline completions and an agent mode. - [Github MCP Server](https://agentscamp.com/tools/github-mcp-server.md): GitHub's official MCP server — repos, issues, PRs, Actions, and security data for your agent, as a free hosted remote or a local Docker server. - [GitMCP](https://agentscamp.com/tools/gitmcp.md): Free, open-source remote MCP server that turns any public GitHub repo or Pages site into docs and code-search tools for your agent. No signup or key. - [Glean](https://agentscamp.com/tools/glean.md): Glean is an enterprise AI search and assistant platform that retrieves company knowledge through configured connectors and source access controls. - [Glide](https://agentscamp.com/tools/glide.md): A no-code platform that turns spreadsheets and databases into mobile and desktop business apps, with an AI Agent that starts the build for you. - [Gong](https://agentscamp.com/tools/gong.md): Gong records and analyses every customer conversation across an org, deriving deal risk, forecast and coaching signals, and publishes three read-only MCP tools. - [Google ADK](https://agentscamp.com/tools/google-adk.md): Google's open-source, code-first framework to build, evaluate, and deploy AI agents. Python and Java, model-agnostic, deploys to Vertex AI Agent Engine. - [Goose](https://agentscamp.com/tools/goose.md): Open-source, on-machine AI agent, originally from Block and now an Agentic AI Foundation project — MCP-native and model-agnostic, with a CLI and desktop app. - [Greptile](https://agentscamp.com/tools/greptile.md): An AI code review agent that reviews pull requests with full-codebase context — catching multi-file logical bugs and learning your team's standards. - [Groq](https://agentscamp.com/tools/groq.md): GroqCloud runs open-weight LLMs on custom LPU hardware for very fast, low-latency inference through an OpenAI-compatible API. - [Helicone](https://agentscamp.com/tools/helicone.md): Open-source LLM observability and AI gateway with one-line integration — logging, tracing, caching, and cost/latency tracking across providers. - [Hex](https://agentscamp.com/tools/hex.md): A collaborative notebook and data-app platform whose AI agents write SQL and Python, answer questions in Threads, and run on curated workspace context. - [Hubspot Breeze](https://agentscamp.com/tools/hubspot-breeze.md): HubSpot's built-in AI: Breeze Assistant in every plan, pre-built Breeze agents for content, prospecting, and support, and a plain-language Agent Builder. - [Ideogram](https://agentscamp.com/tools/ideogram.md): An image generator built around reliable text rendering, with prompt tooling, background control, on-brand custom models, an API, and an MCP server. - [Instructor](https://agentscamp.com/tools/instructor.md): Get structured, validated output from LLMs using plain type definitions, with automatic retries on validation failure. - [Intercom Fin](https://agentscamp.com/tools/intercom-fin.md): Fin by Intercom is an AI customer agent with support-content training, guidance, pre-deployment batch testing, and configured human handover workflows. - [Jan](https://agentscamp.com/tools/jan.md): An open-source ChatGPT alternative that runs fully offline — a polished desktop app over llama.cpp with a model hub, MCP support, and a local API server. - [Jasper](https://agentscamp.com/tools/jasper.md): An enterprise marketing AI workspace with 100+ purpose-built agents, Jasper IQ brand context, Canvas, and an MCP server for other AI tools. - [Jina Reader](https://agentscamp.com/tools/jina-reader.md): Prepend r.jina.ai/ to any URL and get LLM-ready markdown — JS rendering, PDFs and Office docs, image captioning, and s.jina.ai for read-the-results search. - [Jules](https://agentscamp.com/tools/jules.md): Google's asynchronous coding agent: it clones your GitHub repo into a cloud VM, proposes a plan built with Gemini, and opens a pull request once you approve. - [Julius](https://agentscamp.com/tools/julius.md): A chat-first AI data analyst: upload a spreadsheet or connect a warehouse, ask in plain English, and get charts, code, and shareable exports back. - [Kilo Code](https://agentscamp.com/tools/kilo-code.md): Open-source AI coding agent for VS Code and JetBrains, begun as a Roo Code fork and rebuilt on OpenCode, with bring-your-own-key and zero model markup. - [Kiro](https://agentscamp.com/tools/kiro.md): AWS's agentic IDE and CLI that turns a prompt into a spec of requirements, design and tasks, then checks the code against it with property-based tests. - [Label Studio](https://agentscamp.com/tools/label-studio.md): Label Studio is an open-source annotation interface that can present imported or model-generated predictions for people to review and correct. - [LanceDB](https://agentscamp.com/tools/lancedb.md): An open-source embedded vector database built on the Lance columnar format — serverless, multimodal, and designed to scale on local disk or object storage. - [Langchain](https://agentscamp.com/tools/langchain.md): The provider-agnostic agent framework, post-1.0: a standard create_agent loop on the LangGraph runtime, middleware hooks, and the largest integration ecosystem. - [Langfuse](https://agentscamp.com/tools/langfuse.md): An open-source LLM engineering platform for tracing, evals, prompt management, and metrics. - [LangGraph](https://agentscamp.com/tools/langgraph.md): A low-level library for building stateful, controllable agents as graphs, with checkpointing and human-in-the-loop. - [LangSmith](https://agentscamp.com/tools/langsmith.md): LangChain's platform for tracing, evaluating, and monitoring LLM apps — framework-agnostic. - [Letta](https://agentscamp.com/tools/letta.md): Stateful agents from the MemGPT creators — an Apache-2.0 server with self-editing memory, and Letta Code, the memory-first model-agnostic coding harness. - [Lindy](https://agentscamp.com/tools/lindy.md): An AI teammate that lives in Slack, joins your meetings, and runs scheduled routines and reusable skills across 1,000+ tools with approvals. - [Linear MCP](https://agentscamp.com/tools/linear-mcp.md): Linear's hosted MCP server — find, create, and update issues, projects, and comments from your AI agent, with OAuth and one-command setup. - [LiteLLM](https://agentscamp.com/tools/litellm.md): Call 100+ LLM APIs with one OpenAI-format interface — as a Python library or a self-hosted gateway/proxy. - [Livekit](https://agentscamp.com/tools/livekit.md): Open-source realtime infrastructure — a WebRTC server plus the LiveKit Agents framework for production voice AI, with turn detection, telephony, and cloud. - [Llama Cpp](https://agentscamp.com/tools/llama-cpp.md): The C/C++ inference engine that made local LLMs possible — GGUF quantization, every GPU backend, and an OpenAI-compatible server, with no dependencies. - [Llamaindex](https://agentscamp.com/tools/llamaindex.md): The data framework for LLM apps — ingestion, indexing, query engines, and document agents — now centered on document processing with LlamaParse and LlamaCloud. - [LlamaParse](https://agentscamp.com/tools/llamaparse.md): Hosted document-parsing API from LlamaIndex that turns complex PDFs — tables, charts, figures, handwriting — into clean, LLM-ready Markdown for RAG. - [LLM Guard](https://agentscamp.com/tools/llm-guard.md): An open-source security toolkit of input and output scanners for LLM apps — prompt injection, PII/anonymize, secrets, toxicity, and more, from Protect AI. - [LM Studio](https://agentscamp.com/tools/lm-studio.md): A desktop app for discovering, downloading, and running open-weight LLMs locally with a GUI and a local OpenAI-compatible server. - [Loopio](https://agentscamp.com/tools/loopio.md): Loopio is an RFP response platform with a reusable answer library, AI-assisted response drafting, project collaboration, and source-scoped generation controls. - [Lovable](https://agentscamp.com/tools/lovable.md): An AI app builder that turns natural-language prompts into shippable full-stack web apps. - [MagicSchool](https://agentscamp.com/tools/magicschool.md): MagicSchool provides AI lesson, worksheet, quiz and rubric drafts that educators can tailor to supplied readings and instructional goals. - [Marker](https://agentscamp.com/tools/marker.md): Open-source pipeline that converts PDFs, images, and Office docs into clean Markdown, JSON, or HTML fast, with optional LLM assist for tables and equations. - [Mastra](https://agentscamp.com/tools/mastra.md): An open-source TypeScript framework for building AI agents, workflows, RAG, and tool-calling, with memory, model routing, and built-in observability. - [MCP Inspector](https://agentscamp.com/tools/mcp-inspector.md): The official open-source visual tool for testing and debugging Model Context Protocol servers — connect, list, and call tools, resources, and prompts. - [Mem0](https://agentscamp.com/tools/mem0.md): A memory layer for AI agents and apps — persistent, personalized long-term memory across sessions. - [Meshy](https://agentscamp.com/tools/meshy.md): Evaluate Meshy with a versioned prop pilot, owner-set requirements, real inspection reports and actual target-application viewer evidence. - [Microsoft Copilot](https://agentscamp.com/tools/microsoft-copilot.md): Microsoft's work assistant inside Word, Excel, PowerPoint, Outlook, and Teams, grounded in your company's Microsoft 365 data, with Researcher and Analyst. - [Midjourney](https://agentscamp.com/tools/midjourney.md): An image and video generator with a web app and a Discord bot, known for strong aesthetic defaults, style references, an inpainting editor, and paid-only plans. - [Milvus](https://agentscamp.com/tools/milvus.md): An open-source vector database built for billion-scale similarity search, with a distributed architecture and a wide menu of index types. - [Miro](https://agentscamp.com/tools/miro.md): Evaluate Miro as a shared review surface for an approved concept hierarchy and its relationship ledger. - [Mistral OCR](https://agentscamp.com/tools/mistral-ocr.md): Mistral AI's hosted OCR and document-understanding API: PDFs, Office files and images in, per-page Markdown and structured JSON out, billed per page. - [Modal](https://agentscamp.com/tools/modal.md): Serverless AI infrastructure in pure Python — GPU functions with sub-second cold starts, secure sandboxes for agent code, batch jobs, and per-second billing. - [N8n](https://agentscamp.com/tools/n8n.md): Fair-code workflow automation with native AI: a visual canvas plus code, 400+ integrations, and LangChain agent nodes; self-host free or cloud per-run. - [Napkin AI](https://agentscamp.com/tools/napkin-ai.md): Napkin AI turns imported or pasted text into editable visuals for documents and presentations, with export choices that depend on plan. - [NeMo Guardrails](https://agentscamp.com/tools/nemo-guardrails.md): NVIDIA's open-source toolkit for adding programmable guardrails to LLM apps — input, dialog, retrieval, and output rails defined in the Colang language. - [Nooks](https://agentscamp.com/tools/nooks.md): Nooks is an AI sales workspace for outbound teams: a parallel dialer with answer detection, AI sequencing, account research, signals and call coaching. - [Notebooklm](https://agentscamp.com/tools/notebooklm.md): Google's source-grounded research notebook, renamed from NotebookLM in July 2026, that answers only from your uploaded sources and turns them into media. - [Notion MCP](https://agentscamp.com/tools/notion-mcp.md): Notion's hosted MCP server — search the workspace, fetch and create pages and databases, and manage comments through Markdown-optimized agent tools. - [Ollama](https://agentscamp.com/tools/ollama.md): An open-source tool to run open-weight LLMs locally with a single command, including a local OpenAI-compatible API. - [OpenAI Agents SDK](https://agentscamp.com/tools/openai-agents-sdk.md): OpenAI's lightweight, open-source framework for agents — handoffs, guardrails, sessions, and built-in tracing. - [Opencode](https://agentscamp.com/tools/opencode.md): The open-source AI coding agent — a terminal TUI from Anomaly with 75+ model providers, LSP-powered context, parallel agents, and shareable sessions. - [OpenHands](https://agentscamp.com/tools/openhands.md): Open-source autonomous AI software-development agent (formerly OpenDevin) — writes code, runs commands, and browses the web in a sandbox. - [OpenRouter](https://agentscamp.com/tools/openrouter.md): A hosted unified API to hundreds of models from many providers, with one key, one bill, and automatic fallbacks. - [Opus Clip](https://agentscamp.com/tools/opus-clip.md): An AI clipping tool that turns long videos into scored, captioned short clips for Shorts, TikTok, and Reels, with a scheduler, brand templates, and an API. - [Outreach](https://agentscamp.com/tools/outreach.md): Outreach is a sales-execution platform that owns the outbound rep's whole workflow, repackaged around agents including Outreach Omni and Agent Studio. - [Pandasai](https://agentscamp.com/tools/pandasai.md): A Python library that adds a chat method to your dataframes: it generates and runs pandas code to answer questions, with an optional Docker sandbox. - [Parallel Web Systems](https://agentscamp.com/tools/parallel.md): Parallel Web Systems' web APIs for AI agents on its own index: objective-based Search, Extract to markdown, async Task research, and a free keyless MCP. - [Perplexity](https://agentscamp.com/tools/perplexity.md): An AI answer engine that cites its sources on every response, with a Research mode for long reports, Projects for shared context, and the Comet browser. - [pgroll](https://agentscamp.com/tools/pgroll.md): An open-source CLI for zero-downtime, reversible Postgres schema migrations using the expand-contract pattern behind versioned schema views. - [pgvector](https://agentscamp.com/tools/pgvector.md): An open-source Postgres extension that adds a vector type and HNSW/IVFFlat indexes for similarity search inside your existing database. - [Pinecone](https://agentscamp.com/tools/pinecone.md): A fully managed, serverless vector database for similarity search and RAG — no nodes to run, indexes to tune, or infrastructure to operate. - [Pipecat](https://agentscamp.com/tools/pipecat.md): An open-source Python framework for real-time voice and multimodal conversational AI — it orchestrates streaming STT, LLM, and TTS into composable pipelines. - [Playwright MCP](https://agentscamp.com/tools/playwright-mcp.md): Microsoft's open-source MCP server that gives AI agents structured browser automation via Playwright's accessibility tree. - [Portkey](https://agentscamp.com/tools/portkey.md): An AI gateway and LLMOps platform: route to many LLMs through one API with caching, retries, fallbacks, load balancing, guardrails, and full observability. - [Postgres MCP Pro](https://agentscamp.com/tools/postgres-mcp.md): The maintained Postgres MCP server — safe SQL execution, EXPLAIN with hypothetical indexes, workload-driven index tuning, and database health checks. - [promptfoo](https://agentscamp.com/tools/promptfoo.md): An open-source CLI for testing, comparing, and red-teaming LLM prompts, models, and apps. - [Pydantic AI](https://agentscamp.com/tools/pydantic-ai.md): Type-safe agent framework from the Pydantic team: validated structured outputs, dependency injection, durable execution, 'that FastAPI feeling.' - [Qdrant](https://agentscamp.com/tools/qdrant.md): An open-source vector database written in Rust, built for low-latency similarity search at scale. - [Qodo](https://agentscamp.com/tools/qodo.md): A quality-first AI code review platform (ex-CodiumAI) — multi-agent PR review with your team's rules, plus IDE, CLI, and codebase-intelligence products. - [Qwen Code](https://agentscamp.com/tools/qwen-code.md): Alibaba Qwen's Apache-2.0 coding agent for the terminal, editor and desktop: OpenAI, Anthropic, Gemini and Qwen APIs, any provider, or local models. - [Qwen3-VL](https://agentscamp.com/tools/qwen3-vl.md): Alibaba Qwen's open-weights vision-language model family (2B–235B, Apache-2.0): image and document understanding, OCR, visual reasoning, and video. - [RAGAS](https://agentscamp.com/tools/ragas.md): An open-source framework for evaluating retrieval-augmented generation with reference-free RAG metrics. - [Recraft](https://agentscamp.com/tools/recraft.md): An AI image platform built for designers, with editable vector output, reusable custom styles, mockups, and an API for image and vector generation. - [Reducto](https://agentscamp.com/tools/reducto.md): High-accuracy document ingestion API — parsing, agentic OCR, table and figure extraction, and splitting that turns messy PDFs into LLM-ready data for RAG. - [Ref Context](https://agentscamp.com/tools/ref-tools.md): Ref's documentation-search MCP server: search-then-read tools over public docs, your private GitHub repos, and PDFs. Hosted, and an API key is required. - [Relume](https://agentscamp.com/tools/relume.md): An AI site builder that turns a brief into a sitemap, wireframes, and a designed marketing site, then exports to Figma, Webflow, or React. - [RemNote](https://agentscamp.com/tools/remnote.md): RemNote combines notes, AI-generated flashcards and quizzes, and spaced review, with a preview step for checking cards against the source. - [Replicate](https://agentscamp.com/tools/replicate.md): Run and deploy any open ML model — LLMs, image, video, audio — through one API with pay-per-second billing, and package your own with open-source Cog. - [Replit Agent](https://agentscamp.com/tools/replit-agent.md): Replit's AI agent that builds, runs, and deploys full-stack apps from a prompt inside the Replit cloud IDE. - [Repomix](https://agentscamp.com/tools/repomix.md): MIT tool that packs a local codebase or GitHub repo into one AI-friendly file, as a CLI or an MCP server your agent can grep and read in slices. - [Roo Code](https://agentscamp.com/tools/roo-code.md): A discontinued open-source VS Code agent (a Cline fork); the team has since pivoted away from the IDE extension. - [Rork](https://agentscamp.com/tools/rork.md): An AI mobile app builder that turns a chat prompt into native iPhone (Swift), Android (Kotlin), and web apps you can publish to the App Store. - [Scribe](https://agentscamp.com/tools/scribe.md): Scribe captures on-screen workflows into editable visual guides and adds AI drafting features for titles, descriptions, and process documentation. - [Sentry MCP](https://agentscamp.com/tools/sentry-mcp.md): Sentry's hosted MCP service for debugging — pull issues, events, traces, and releases into your agent, and trigger Seer root-cause analysis. - [Sequential Thinking MCP](https://agentscamp.com/tools/sequential-thinking-mcp.md): The official MCP reference server for structured reasoning — a sequential_thinking tool that lets agents decompose, revise, and branch their thinking. - [Serena](https://agentscamp.com/tools/serena.md): An MCP toolkit that gives coding agents IDE-grade powers — symbol-level retrieval and editing via language servers, across 40+ languages. - [Skyvern](https://agentscamp.com/tools/skyvern.md): Open-source vision + LLM browser automation aimed at replacing brittle RPA — workflow builder, CAPTCHA/2FA handling, and self-host or cloud. - [Slack MCP Server](https://agentscamp.com/tools/slack-mcp.md): The community-canonical Slack MCP server — smart history fetch, message search, channels, reactions, and opt-in posting, after the official server was archived. - [Smithery](https://agentscamp.com/tools/smithery.md): A registry and hosting platform for Model Context Protocol servers — discover, deploy, and connect MCP servers from one place. - [Softr](https://agentscamp.com/tools/softr.md): A no-code builder for client portals and internal tools on top of Airtable, Google Sheets, or its own database, now with AI app generation. - [Spec Kit](https://agentscamp.com/tools/spec-kit.md): GitHub's open-source toolkit for spec-driven development — a specify CLI and /speckit slash commands that walk any coding agent from spec to implementation. - [Spellbook](https://agentscamp.com/tools/spellbook.md): Spellbook supports legal contract work with AI questions, source references, Microsoft Word review suggestions, and reusable contract-review instructions. - [Stagehand](https://agentscamp.com/tools/stagehand.md): Browserbase's open-source SDK for browser agents — act, extract, observe, and agent primitives that mix natural language with code-level control. - [Stitch](https://agentscamp.com/tools/stitch.md): Google's AI design canvas that turns prompts, voice, images, or an existing codebase into high-fidelity mobile and web UI, with frontend code and a Figma paste. - [Stripe MCP](https://agentscamp.com/tools/stripe-mcp.md): Stripe's official MCP server — customers, invoices, payment links, subscriptions, refunds, and docs search for agents, hosted at mcp.stripe.com. - [Supabase MCP](https://agentscamp.com/tools/supabase-mcp.md): Supabase's official MCP server — run SQL and migrations, read logs and advisors, generate types, and deploy Edge Functions, with read-only and project scoping. - [Surfer](https://agentscamp.com/tools/surfer.md): An SEO and AI-visibility platform whose Content Editor scores drafts against live SERP guidelines, with AI writing, audits, and AI-search tracking. - [Surveymonkey](https://agentscamp.com/tools/surveymonkey.md): SurveyMonkey combines AI survey drafting with editable survey questions and plan-dependent skip logic for respondent paths. - [Swe Agent](https://agentscamp.com/tools/swe-agent.md): Open-source autonomous coding agent from Princeton/Stanford that turns an LLM into a software engineer to fix real GitHub issues. - [Tabby](https://agentscamp.com/tools/tabby.md): Self-hosted, open-source AI coding assistant by TabbyML — run your own completion and chat models on your infrastructure, with IDE extensions. - [Tabnine](https://agentscamp.com/tools/tabnine.md): An AI code completion and chat assistant built around code privacy, self-hosting, and air-gapped enterprise deployment. - [Tamr](https://agentscamp.com/tools/tamr.md): Evaluate Tamr with a bounded mastering pilot that preserves source keys, exposes uncertain candidates and names the owner of each decision. - [Tavily](https://agentscamp.com/tools/tavily.md): The web-access layer for agents — Search, Extract, Crawl, Map, and Research APIs purpose-built for LLMs, behind one key, with a hosted MCP server. - [Thoughtspot Spotter](https://agentscamp.com/tools/thoughtspot-spotter.md): ThoughtSpot's agentic analyst: it resolves questions into search tokens against a governed semantic model rather than raw SQL, then acts on the answer. - [Together AI](https://agentscamp.com/tools/together-ai.md): A cloud for running, fine-tuning, and deploying open-source models (Llama, DeepSeek, Qwen) via an OpenAI-compatible API plus dedicated GPU endpoints. - [Trae](https://agentscamp.com/tools/trae.md): Trae is an AI-native IDE from ByteDance — a VS Code-style editor with a built-in Builder agent and an autonomous SOLO mode that writes code across a project. - [turbopuffer](https://agentscamp.com/tools/turbopuffer.md): A serverless vector and full-text search database built on object storage (S3/GCS/Azure) — usage-based pricing, hybrid search, and low cost per GB at scale. - [Typeform](https://agentscamp.com/tools/typeform.md): Typeform builds online forms with AI-assisted questions, endings and branching rules that owners can preview and revise. - [Unify](https://agentscamp.com/tools/unify.md): Unify turns real-time intent signals into automated, AI-personalised prospecting plays, with a chat agent, 50+ native skills and flat per-seat pricing. - [Unsloth](https://agentscamp.com/tools/unsloth.md): An open-source library that makes LoRA/QLoRA fine-tuning of LLMs roughly 2x faster and far more memory-efficient, so you can fine-tune on a single GPU. - [Unstructured](https://agentscamp.com/tools/unstructured.md): Open-source library plus hosted Unstructured Pipelines and API that turn messy documents — PDF, HTML, docx, images, email — into clean, chunked JSON for RAG. - [V0](https://agentscamp.com/tools/v0.md): Vercel's generative UI builder that turns prompts into production-ready React, Next.js, and shadcn/ui apps. - [Vanna](https://agentscamp.com/tools/vanna.md): An MIT-licensed Python framework for text-to-SQL: a user-aware agent that learns from successful queries and streams tables, charts, and summaries back. - [Vapi](https://agentscamp.com/tools/vapi.md): The API-first voice-agent platform — assemble phone and web agents from any STT/LLM/TTS mix, with telephony, squads, and tool calling handled for you. - [VEED](https://agentscamp.com/tools/veed.md): VEED is a browser video editor with AI-generated subtitles, editable text and timing, and plan-dependent caption-file downloads. - [Vercel AI SDK](https://agentscamp.com/tools/vercel-ai-sdk.md): An open-source TypeScript toolkit for building AI apps — unified model API, streaming, structured output, tool calling, and UI hooks. - [Vercel Sandbox](https://agentscamp.com/tools/vercel-sandbox.md): Ephemeral Firecracker microVMs on Vercel for untrusted and AI-generated code — millisecond startup, Node and Python runtimes, persistent by default. - [vLLM](https://agentscamp.com/tools/vllm.md): A high-throughput, memory-efficient inference and serving engine for LLMs, with PagedAttention, continuous batching, and an OpenAI-compatible API server. - [Void — Open-Source AI Code Editor (VS Code Fork)](https://agentscamp.com/tools/void.md): Open-source AI code editor forked from VS Code, an alternative to Cursor that connects directly to your chosen model with no proprietary backend. - [Voyage AI](https://agentscamp.com/tools/voyage-ai.md): Embedding and reranking models tuned for retrieval, now part of MongoDB. - [Warp](https://agentscamp.com/tools/warp.md): A modern, AI-powered terminal with an agent mode that can run and chain commands across your codebase. - [Wave Terminal](https://agentscamp.com/tools/wave-terminal.md): Open-source terminal that blends the CLI with inline file previews, a built-in editor, a web browser, and a context-aware AI assistant in one window. - [Weaviate](https://agentscamp.com/tools/weaviate.md): An open-source vector database with built-in hybrid search, pluggable vectorizer modules, and GraphQL/REST/gRPC APIs. - [Whisper](https://agentscamp.com/tools/whisper.md): OpenAI's open-weights speech-to-text — the MIT-licensed multilingual model family that made self-hosted transcription a default, with a huge ecosystem. - [Devin Desktop (formerly Windsurf)](https://agentscamp.com/tools/windsurf.md): An agentic IDE — formerly Windsurf, now Devin Desktop from Cognition AI — with flows that take multi-step actions across your codebase. - [Workable](https://agentscamp.com/tools/workable.md): Workable combines recruiting and HR software with AI drafting tools, configurable interview kits, shared rating scales, and candidate feedback records. - [Zapier Agents](https://agentscamp.com/tools/zapier-agents.md): Zapier's no-code AI agents that take plain-English instructions and act across 9,000+ apps on a schedule, on app events, or on demand. - [Zed](https://agentscamp.com/tools/zed.md): A high-performance, multiplayer code editor with built-in AI assistance. - [Zep](https://agentscamp.com/tools/zep.md): Agent memory on temporal knowledge graphs — Zep Cloud for sub-200ms context retrieval at enterprise scale, with Graphiti as its open-source graph engine. - [Zoominfo](https://agentscamp.com/tools/zoominfo.md): ZoomInfo is a B2B contact and company database with intent signals, now positioned as the grounding data your agent calls through API, MCP or CLI. ## Glossary Plain-language definitions of the AI and LLM-engineering terms you'll meet across the hub — answer-first, with the deeper guide linked. - [A2A (Agent2Agent Protocol)](https://agentscamp.com/glossary/a2a-protocol.md): A2A is an open protocol that lets AI agents discover each other's capabilities and delegate tasks across vendors, complementing MCP's tool connections. - [Agent Engineering](https://agentscamp.com/glossary/agent-engineering.md): Agent engineering is the discipline of building reliable AI agents — designing the tools, context, guardrails, evals, and recovery paths around the model. - [Agent Harness](https://agentscamp.com/glossary/agent-harness.md): An agent harness is the system around the model that makes it an agent — the loop, tools, context management, permissions, and recovery machinery. - [Agent Memory](https://agentscamp.com/glossary/agent-memory.md): Agent memory is how an AI agent retains information beyond its context window — working state during a task and persistent knowledge across sessions. - [Agent Skills](https://agentscamp.com/glossary/agent-skills.md): Agent Skills are reusable procedures packaged as folders with a SKILL.md file — loaded by an AI agent on demand when a task matches, now an open standard. - [Agentic AI](https://agentscamp.com/glossary/agentic-ai.md): Agentic AI is the class of AI systems that act toward goals — planning, calling tools, and iterating on results — rather than only generating content. - [AGENTS.md](https://agentscamp.com/glossary/agents-md.md): AGENTS.md is a versioned instruction file that tells coding agents how to work in a repository — including commands, conventions, boundaries, and checks. - [AI Agent](https://agentscamp.com/glossary/ai-agent.md): An AI agent is an LLM-driven system that pursues a goal in a loop — calling tools, observing results, iterating — instead of returning one answer. - [AI App Builder](https://agentscamp.com/glossary/ai-app-builder.md): An AI app builder is a tool that generates a working application from a natural-language description, usually with hosting and a backend included. - [AI Connectors](https://agentscamp.com/glossary/ai-connectors.md): AI connectors are the integrations that let an assistant like Claude read and act in tools such as Google Drive, Slack, and Notion; in Claude, MCP servers. - [AI Data Analyst](https://agentscamp.com/glossary/ai-data-analyst.md): An AI data analyst is a tool that takes a data question, writes and runs the code or SQL to answer it, and returns a chart or summary you still have to check. - [AI Overviews](https://agentscamp.com/glossary/ai-overviews.md): AI Overviews are Google's AI-generated summaries at the top of Search results, with source links; AI Mode is the fuller conversational version. - [AI SDR](https://agentscamp.com/glossary/ai-sdr.md): An AI SDR is software that runs sales development work such as prospecting, outreach, and follow-up, with autonomy that varies enormously between vendors. - [AI Slop](https://agentscamp.com/glossary/ai-slop.md): AI slop is low-effort, mass-produced AI-generated content — fluent, generic, and unchecked — flooding feeds, search results, and codebases. - [AI Wrapper](https://agentscamp.com/glossary/ai-wrapper.md): An AI wrapper is a product whose core function is a prompt and interface around a third-party model API, such as Claude or GPT, rather than its own model. - [Alternative Text](https://agentscamp.com/glossary/alternative-text.md): Choose useful wording for an image placement by keeping inspected observations, page purpose and human approval together. - [Animatic](https://agentscamp.com/glossary/animatic.md): An animatic is a timed preview of storyboard frames, often with audio, used to review pacing before production; AI-generated frames remain drafts. - [Answer Engine Optimization (AEO)](https://agentscamp.com/glossary/answer-engine-optimization.md): Answer engine optimization (AEO) is structuring content so search engines and AI assistants can lift a direct answer from it for AI Overviews and chatbots. - [Attention Mechanism](https://agentscamp.com/glossary/attention-mechanism.md): Attention lets a model weigh how relevant every other token is to each token, building a context-aware representation as a weighted blend of their values. - [Automatic captioning](https://agentscamp.com/glossary/automatic-captioning.md): Automatic captioning uses speech recognition to produce timed text for media; human review checks meaning, speaker cues and meaningful sounds. - [Batch Inference](https://agentscamp.com/glossary/batch-inference.md): Batch inference processes many LLM requests asynchronously instead of one-at-a-time interactively — typically at ~50% discount via provider batch APIs. - [Blocking in Record Matching](https://agentscamp.com/glossary/blocking-record-matching.md): Understand how a candidate-selection rule can miss known pairs and why missing fields need coverage checks before identity review. - [Brand Voice (in AI prompting)](https://agentscamp.com/glossary/brand-voice.md): Brand voice, in AI prompting, is the documented tone, vocabulary, and style rules a model is given so every draft sounds like one company, not the model. - [Chain-of-Thought (CoT)](https://agentscamp.com/glossary/chain-of-thought.md): Chain-of-thought prompting has a model work through intermediate reasoning steps before answering — improving accuracy on multi-step problems. - [Chunking](https://agentscamp.com/glossary/chunking.md): Chunking splits documents into retrievable pieces before embedding — the RAG design decision that quietly determines retrieval quality. - [Claude Cowork](https://agentscamp.com/glossary/claude-cowork.md): Claude Cowork is the agentic mode of Anthropic's Claude app that completes multi-step knowledge-work tasks in your files, connectors, and browser. - [Claude Plugins](https://agentscamp.com/glossary/claude-plugins.md): Claude Plugins are installable bundles of skills, slash commands, sub-agents, and MCP connectors that set up Claude Code or Claude Cowork for a role or team. - [Code Execution (Code Interpreter)](https://agentscamp.com/glossary/code-execution.md): Code execution lets an AI assistant write and run real code in a sandbox, so numbers, files, and charts come from a computation rather than an estimate. - [Compliance Matrix](https://agentscamp.com/glossary/compliance-matrix.md): An RFP compliance matrix maps each buyer requirement to its source, response location, evidence, owner, and reviewed status throughout a proposal. - [Computer Use](https://agentscamp.com/glossary/computer-use.md): Computer use is an AI agent operating software through its real interface — reading the screen, moving the cursor, clicking, and typing like a person would. - [Constitutional AI](https://agentscamp.com/glossary/constitutional-ai.md): Constitutional AI trains models against written principles — the model critiques and revises its own outputs by them, reducing reliance on human labels. - [Context Engineering](https://agentscamp.com/glossary/context-engineering.md): Context engineering is the discipline of curating exactly what enters an LLM's context window so it has the right information and nothing else. - [Context Window](https://agentscamp.com/glossary/context-window.md): The context window is the maximum text — measured in tokens — an LLM can consider at once: prompt, conversation, documents, and its own output combined. - [Contract Abstraction](https://agentscamp.com/glossary/contract-abstraction.md): Contract abstraction extracts selected parties, dates, obligations, and terms from an agreement into a structured summary that retains source references. - [Conversation Intelligence](https://agentscamp.com/glossary/conversation-intelligence.md): Conversation intelligence is recording sales calls and meetings, transcribing them, and turning transcripts into searchable notes, actions, and CRM records. - [Conversational Analytics](https://agentscamp.com/glossary/conversational-analytics.md): Conversational analytics is asking questions of governed business data in plain language and getting a chart or number back, without opening a dashboard. - [Cosine Similarity](https://agentscamp.com/glossary/cosine-similarity.md): Cosine similarity measures how alike two embeddings are by the angle between them — the standard relevance score behind semantic search and RAG retrieval. - [Data Lineage](https://agentscamp.com/glossary/data-lineage.md): Data lineage records where data comes from, how it changes, and where it is used, helping teams trace an AI output back through its inputs. - [Design-to-Code](https://agentscamp.com/glossary/design-to-code.md): Design-to-code is turning a design — a Figma file, a screenshot, or a generated mockup — into working front-end code, increasingly by an AI agent. - [Design Tokens](https://agentscamp.com/glossary/design-tokens.md): Design tokens are named values for a design system's decisions — color, spacing, type, radius — stored once and referenced everywhere instead of hardcoded. - [Diffusion Model](https://agentscamp.com/glossary/diffusion-model.md): A diffusion model generates an image by starting from random noise and removing it step by step, guided by a prompt, until a coherent picture remains. - [Digital Asset Management](https://agentscamp.com/glossary/digital-asset-management.md): A catalog-review concept for separating files, descriptive metadata and supplied approval records before reuse. - [Distillation](https://agentscamp.com/glossary/distillation.md): Distillation trains a smaller model to imitate a larger one — using its outputs as training data to get most of the capability at a fraction of the cost. - [DPO (Direct Preference Optimization)](https://agentscamp.com/glossary/dpo.md): DPO aligns a model to preferences directly from chosen-vs-rejected pairs — no reward model, no RL loop — simpler and more stable than classic RLHF. - [Embedding Dimension](https://agentscamp.com/glossary/embedding-dimension.md): Embedding dimension is the length of an embedding vector — how many numbers represent each text — trading capacity against storage and search cost. - [Embedding](https://agentscamp.com/glossary/embedding.md): An embedding is a vector of numbers representing text's meaning, placed so similar texts land close together — the foundation of semantic search and RAG. - [Entity Resolution](https://agentscamp.com/glossary/entity-resolution.md): Understand the identity question behind similar records, including false merges, missed matches and the source owner’s unresolved decisions. - [Eval Dataset](https://agentscamp.com/glossary/eval-dataset.md): An eval dataset is the curated set of test cases — inputs with expected outcomes — that an LLM application's quality is measured against. - [Evidence Synthesis](https://agentscamp.com/glossary/evidence-synthesis.md): Evidence synthesis brings findings from multiple studies together to answer a research question while retaining differences, limitations, and uncertainty. - [Extended Thinking](https://agentscamp.com/glossary/extended-thinking.md): Extended thinking is the reasoning tokens a model generates before its final answer, trading latency and cost for higher accuracy on hard problems. - [Few-Shot Prompting](https://agentscamp.com/glossary/few-shot-prompting.md): Few-shot prompting includes worked examples in the prompt so the model learns the task's pattern from demonstrations instead of instructions alone. - [Fine-Tuning](https://agentscamp.com/glossary/fine-tuning.md): Fine-tuning continues training a pretrained model on your own examples, changing its weights to teach durable behavior, format, or domain style. - [Flash Attention](https://agentscamp.com/glossary/flash-attention.md): FlashAttention is an IO-aware, exact attention algorithm that runs standard attention far faster and with less memory by tiling on-chip. - [Formative assessment](https://agentscamp.com/glossary/formative-assessment.md): Formative assessment gathers feedback during learning so teachers and learners can adjust next steps; an AI draft still needs educator review. - [Frontier Model](https://agentscamp.com/glossary/frontier-model.md): A frontier model is one of the most capable AI models available — the leading edge from labs like Anthropic, OpenAI, and Google, defining the state of the art. - [Function Calling (Tool Calling)](https://agentscamp.com/glossary/function-calling.md): Function calling lets an LLM request structured invocations of your code: describe tools with schemas, the model emits typed calls, your app executes them. - [Generative Engine Optimization (GEO)](https://agentscamp.com/glossary/generative-engine-optimization.md): Generative engine optimization (GEO) is making content more likely to be cited or summarized in AI answers from ChatGPT, Gemini, or Google AI Overviews. - [Generative UI](https://agentscamp.com/glossary/generative-ui.md): Generative UI is an interface produced by a model from a prompt — a whole screen, flow, or prototype generated rather than assembled by hand. - [Grounding](https://agentscamp.com/glossary/grounding.md): Grounding ties a model's output to verifiable sources — retrieved documents, tool results, citations — instead of training-data memory. - [Guardrails](https://agentscamp.com/glossary/guardrails.md): Guardrails are programmatic checks around an LLM — validating inputs and outputs in code — enforcing safety and format rules a prompt alone can't guarantee. - [Hallucination](https://agentscamp.com/glossary/hallucination.md): A hallucination is fluent, confident output that is factually wrong or fabricated — plausible text unsupported by any source, the signature LLM failure mode. - [Human-in-the-Loop (HITL)](https://agentscamp.com/glossary/human-in-the-loop.md): Human-in-the-loop design inserts human judgment at decisive points in an AI workflow — approving actions, resolving ambiguity, owning the irreversible steps. - [Hybrid Search](https://agentscamp.com/glossary/hybrid-search.md): Hybrid search runs keyword (BM25) and semantic (vector) retrieval together and merges the results — catching both exact terms and paraphrases. - [Inference](https://agentscamp.com/glossary/inference.md): Inference is running a trained model to produce output — for LLMs, generating tokens one at a time. Its cost and latency define the economics of AI products. - [Jailbreak](https://agentscamp.com/glossary/jailbreak.md): A jailbreak is a prompt crafted to bypass a model's safety training and policies — making it produce output it was trained to refuse. - [Knowledge Cutoff](https://agentscamp.com/glossary/knowledge-cutoff.md): A knowledge cutoff is the date a model's training data ends, so it has no built-in knowledge of any event, release, or fact that came after it. - [KV Cache](https://agentscamp.com/glossary/kv-cache.md): The KV cache stores each token's attention keys and values so an LLM doesn't recompute the whole context per new token — the memory that makes generation fast. - [Lead Enrichment](https://agentscamp.com/glossary/lead-enrichment.md): Lead enrichment is filling the missing fields on a lead or account record, such as title, headcount, or tech stack, from data sources outside your own system. - [LLM-as-Judge](https://agentscamp.com/glossary/llm-as-judge.md): LLM-as-judge uses a language model to score AI outputs against a rubric — evaluating quality at scale where exact-match metrics fail and humans don't scale. - [Token (LLM)](https://agentscamp.com/glossary/llm-token.md): A token is the unit LLMs read and write — a word fragment of roughly 3–4 characters in English. Models are priced, limited, and measured in tokens, not words. - [LLMOps](https://agentscamp.com/glossary/llmops.md): LLMOps is the practices and tooling for running LLM apps in production: prompt versioning, evals, tracing, cost and latency monitoring, and guardrails. - [llms.txt](https://agentscamp.com/glossary/llms-txt.md): llms.txt is a proposed Markdown file at a site's root that gives AI models a curated summary and link list, so they can find the right pages at inference time. - [LoRA (Low-Rank Adaptation)](https://agentscamp.com/glossary/lora.md): LoRA fine-tunes a model by training small low-rank adapter matrices instead of all weights — a fraction of the memory and cost, nearly full-tune quality. - [Machine Translation Post-editing](https://agentscamp.com/glossary/machine-translation-post-editing.md): Machine translation post-editing is human revision of machine-translated text against the source, context, terminology, and agreed quality requirements. - [Mixture of Experts (MoE)](https://agentscamp.com/glossary/mixture-of-experts.md): MoE is a model architecture where a router activates only a few expert subnetworks per token — huge total capacity, a fraction of the compute per token. - [MCP (Model Context Protocol)](https://agentscamp.com/glossary/model-context-protocol.md): MCP is the open standard for connecting AI models to external tools and data: write one server, and any MCP client — Claude Code, IDEs, agents — can use it. - [Model Routing](https://agentscamp.com/glossary/model-routing.md): Model routing sends each request to the cheapest model that can handle it, escalating only hard cases to a stronger model — cutting cost and latency. - [Multimodal AI](https://agentscamp.com/glossary/multimodal-ai.md): Multimodal AI processes more than one kind of input or output — text, images, audio, video — in a single model, like an LLM that reads screenshots or speaks. - [Needle in a Haystack](https://agentscamp.com/glossary/needle-in-a-haystack.md): Needle in a haystack is a long-context eval that hides a fact in filler text and tests whether the model can retrieve it at varying depths and lengths. - [No-Code AI](https://agentscamp.com/glossary/no-code-ai.md): No-code AI is building software or automations that use AI models through visual tools and prompts, without writing or maintaining code. - [Open Weights](https://agentscamp.com/glossary/open-weights.md): An open-weights model publishes its parameters for anyone to download and run — unlike API-only models — with licenses from permissive to restricted. - [Optical character recognition (OCR)](https://agentscamp.com/glossary/optical-character-recognition.md): Optical character recognition converts text pictured in a scan or image into machine-readable characters; recognition output still needs source checks. - [Perplexity](https://agentscamp.com/glossary/perplexity.md): Perplexity measures how well a language model predicts a text sample — the exponential of its average per-token negative log-likelihood. Lower is better. - [Physically Based Rendering](https://agentscamp.com/glossary/physically-based-rendering.md): Review an AI material handoff without confusing supplied map files with verified appearance in the target scene. - [Process Capture](https://agentscamp.com/glossary/process-capture.md): Process capture records how a task is actually performed, preserving observed actions and context as input to a reviewed procedure or training guide. - [Prompt Caching](https://agentscamp.com/glossary/prompt-caching.md): Prompt caching reuses the computed state of a repeated prompt prefix across requests — dramatically cutting cost and time-to-first-token for stable context. - [Prompt Engineering](https://agentscamp.com/glossary/prompt-engineering.md): Prompt engineering is the practice of designing an LLM's inputs — instructions, context, examples, and format — to reliably get the output you want. - [Prompt Injection](https://agentscamp.com/glossary/prompt-injection.md): Prompt injection is an attack where untrusted content carries instructions an LLM then follows — overriding its task, leaking data, or triggering tool calls. - [Prompt Template](https://agentscamp.com/glossary/prompt-template.md): A prompt template is a parameterized prompt — fixed instructions with variable slots — turning prompts from strings into versioned, testable components. - [Quantization](https://agentscamp.com/glossary/quantization.md): Quantization shrinks a model by storing weights in lower precision (8-, 4-, even 2-bit) — cutting memory and speeding inference at a small accuracy cost. - [RAG (Retrieval-Augmented Generation)](https://agentscamp.com/glossary/rag.md): RAG retrieves relevant documents from your own data and injects them into an LLM's prompt at query time, grounding answers in facts the model wasn't trained on. - [ReAct (Reasoning + Acting)](https://agentscamp.com/glossary/react-agent.md): ReAct is an agent loop that interleaves reasoning with tool actions — Thought, Action, Observation, repeat — so the model plans, calls a tool, and revises. - [Reasoning Model](https://agentscamp.com/glossary/reasoning-model.md): A reasoning model is an LLM trained to think before answering — generating internal reasoning tokens it can spend adaptively on hard problems. - [Record Linkage](https://agentscamp.com/glossary/record-linkage.md): Understand the reviewable crosswalk needed before joining sources with unrelated keys, ambiguous candidates and explicit cardinality policies. - [Red-Teaming (AI)](https://agentscamp.com/glossary/red-teaming.md): AI red-teaming is adversarial testing — attacking your model or agent with jailbreaks, injections, and misuse scenarios to find failures before users do. - [Reranking](https://agentscamp.com/glossary/reranking.md): Reranking is a second-pass scoring step: a cross-encoder model re-orders the top results from fast retrieval so the truly relevant few rise to the top. - [Revenue Intelligence](https://agentscamp.com/glossary/revenue-intelligence.md): Revenue intelligence is deriving deal risk, forecast accuracy, and coaching signals from captured customer conversations rather than from CRM fields. - [RLHF (Reinforcement Learning from Human Feedback)](https://agentscamp.com/glossary/rlhf.md): RLHF trains a model against human preferences: people rank outputs, a reward model learns the ranking, and the LLM is optimized to produce preferred responses. - [Sales Engagement Platform](https://agentscamp.com/glossary/sales-engagement-platform.md): A sales engagement platform is the system reps work out of: it sequences emails, calls, and tasks across channels and logs every touch back into the CRM. - [Semantic Caching](https://agentscamp.com/glossary/semantic-caching.md): Semantic caching reuses LLM responses keyed by meaning rather than exact text, matching queries by embedding similarity to cut cost and latency. - [Semantic Layer](https://agentscamp.com/glossary/semantic-layer.md): A semantic layer defines business metrics, dimensions, and joins once, so every query and every AI agent computes the same number the same way. - [Semantic Search](https://agentscamp.com/glossary/semantic-search.md): Semantic search retrieves results by meaning rather than keyword overlap — embedding queries and documents in one vector space and matching by similarity. - [Skip Logic](https://agentscamp.com/glossary/skip-logic.md): Skip logic routes a respondent to different survey questions or endings based on answers or other stated conditions. - [SLM (Small Language Model)](https://agentscamp.com/glossary/small-language-model.md): A small language model is a compact LLM — roughly 1–15B parameters — that runs cheaply or locally, trading peak capability for speed and deployability. - [Spaced repetition](https://agentscamp.com/glossary/spaced-repetition.md): Spaced repetition schedules reviews over time using recall feedback; AI can draft the cards, while the learner still checks their answers. - [Speculative Decoding](https://agentscamp.com/glossary/speculative-decoding.md): Speculative decoding speeds up generation: a small draft model proposes tokens, the large model verifies them in one parallel pass — same output, fewer steps. - [Speech enhancement](https://agentscamp.com/glossary/speech-enhancement.md): Speech enhancement reduces unwanted noise or artifacts in recorded speech; AI-processed audio still needs comparison with the original for intelligibility. - [Structured Interview](https://agentscamp.com/glossary/structured-interview.md): A structured interview uses predetermined job-related questions and shared evaluation standards so interviewers collect comparable evidence from candidates. - [Structured Output](https://agentscamp.com/glossary/structured-output.md): Structured output makes an LLM return data in a guaranteed shape — JSON matching your schema — so code can consume model responses without parsing prose. - [Subagent](https://agentscamp.com/glossary/subagent.md): A subagent is a specialist agent a primary agent delegates to — running in its own context window with its own prompt and tools, returning only a summary. - [Synthetic Data](https://agentscamp.com/glossary/synthetic-data.md): Synthetic data is training or eval data generated by a model rather than collected from the world — filling gaps, balancing classes, bootstrapping fine-tunes. - [System Prompt](https://agentscamp.com/glossary/system-prompt.md): The system prompt is the standing instruction layer an LLM receives before user input — defining its role, rules, tools, and tone for the whole conversation. - [Temperature](https://agentscamp.com/glossary/temperature.md): Temperature controls how random an LLM's token choices are: low values make output focused and repeatable, high values make it varied and creative. - [Test-Time Compute](https://agentscamp.com/glossary/test-time-compute.md): Test-time compute is spending more computation at inference — longer reasoning, sampling, or search — to improve answers without retraining the model. - [Text-to-Image](https://agentscamp.com/glossary/text-to-image.md): Text-to-image is generating a picture from a written prompt, using a model trained to turn a description into pixels — or, in some tools, vectors. - [Text-to-SQL](https://agentscamp.com/glossary/text-to-sql.md): Text-to-SQL is turning a plain-language question into a SQL query a database can run, using a model grounded in your schema, documentation, and past queries. - [Thematic Analysis](https://agentscamp.com/glossary/thematic-analysis.md): Thematic analysis interprets patterns of meaning in qualitative data, such as interview transcripts, open survey answers, and customer feedback. - [Ticket Deflection](https://agentscamp.com/glossary/ticket-deflection.md): Ticket deflection means helping a customer find an answer through self-service before they submit a support ticket or need a human support queue. - [Token Streaming](https://agentscamp.com/glossary/token-streaming.md): Token streaming delivers model output incrementally as it's generated — via SSE or websockets — so users see text immediately instead of waiting. - [Tokenization](https://agentscamp.com/glossary/tokenization.md): Tokenization splits text into tokens — the sub-word units a model reads and writes — and maps each to an integer ID the model processes. - [Top-k Sampling](https://agentscamp.com/glossary/top-k.md): Top-k sampling restricts an LLM's next-token choice to the k most probable tokens before sampling; lower k is more deterministic, higher k more diverse. - [Top-p (Nucleus Sampling)](https://agentscamp.com/glossary/top-p.md): Top-p sampling restricts an LLM's next-token choices to the smallest set whose probabilities sum to p — cutting the long tail of unlikely tokens adaptively. - [Tracing (LLM)](https://agentscamp.com/glossary/tracing.md): LLM tracing records every step of a model-driven request — prompts, tool calls, retrievals, tokens, latency — so multi-step behavior is debuggable. - [Transformer](https://agentscamp.com/glossary/transformer.md): The neural-network architecture (Vaswani et al., 2017) that uses self-attention to process sequences in parallel — the basis of nearly all modern LLMs. - [Tree of Thoughts](https://agentscamp.com/glossary/tree-of-thoughts.md): Tree of Thoughts is a prompting method that explores multiple reasoning branches as a search tree, evaluating and backtracking among them. - [UV Mapping](https://agentscamp.com/glossary/uv-mapping.md): Understand why a 3D handoff keeps mesh, surface-layout and texture versions together before approving their actual target appearance. - [Vector Database](https://agentscamp.com/glossary/vector-database.md): A vector database stores embeddings and answers nearest-neighbor queries fast — the retrieval layer under RAG and semantic search, using ANN indexes like HNSW. - [Vibe Coding](https://agentscamp.com/glossary/vibe-coding.md): Vibe coding is building software by describing intent in natural language and letting an AI agent write the code, judging results by behavior. - [VLM (Vision-Language Model)](https://agentscamp.com/glossary/vision-language-model.md): A VLM jointly understands images and text — reading documents, screenshots, charts, and photos and reasoning about them in language. - [AI Workflow Automation](https://agentscamp.com/glossary/workflow-automation.md): AI workflow automation is connecting apps so tasks run on triggers, with an AI model handling the judgment steps a fixed rule cannot express. - [Zero-Shot Prompting](https://agentscamp.com/glossary/zero-shot-prompting.md): Zero-shot prompting asks a model to perform a task from instructions alone, with no examples — the default mode for capable modern LLMs. ## Agents Drop-in Claude Code subagents with focused system prompts — code review, debugging, architecture, and more. - [Analysis Reviewer](https://agentscamp.com/agents/analytics/analysis-reviewer.md): Use this agent to review a finished analysis for methodological errors before it ships — checking grain and double counting, join fan-out, rows silently dropped by filters and inner joins, sampling and truncation, null handling, time zone and date boundaries, numbers in the prose that disagree with the code's output, charts that mislead, and causal language resting on correlational evidence. Examples — 'review this notebook before I send the deck', 'the query and the summary disagree somewhere, find it', 'does this analysis actually support the conclusion it draws?'. - [Annotation Evidence Reviewer](https://agentscamp.com/agents/analytics/annotation-evidence-reviewer.md): Review a supervised text-label adjudication packet for traceable excerpts, preserved independent responses and rule-supported resolutions; flag missing evidence without choosing final labels. - [Entity Match Reviewer](https://agentscamp.com/agents/analytics/entity-match-reviewer.md): Review an entity-match candidate ledger against supplied records and owner criteria; flag unsupported match decisions, conflicting fields and transitive contradictions without merging or identifying people. - [Exit Ticket Reviewer](https://agentscamp.com/agents/analytics/exit-ticket-reviewer.md): Review a draft exit ticket against educator-supplied objectives and taught material. Flag untaught facts, unclear prompts, mismatched answer keys and gaps in objective coverage; return revisions for educator judgment without grading learners or diagnosing ability. - [Literature Screening Reviewer](https://agentscamp.com/agents/analytics/literature-screening-reviewer.md): Use this agent to audit a literature screening log against a supplied protocol and local abstracts or paper extracts: trace each eligibility judgment to criteria and source text, flag unsupported exclusions, identify unresolved eligibility, and record protocol drift. Review the log without searching the web or changing inclusion decisions. - [OCR Fidelity Reviewer](https://agentscamp.com/agents/analytics/ocr-fidelity-reviewer.md): Review supplied OCR text and readable source-page images for transcription fidelity. Flag changed characters, reading-order errors and unsupported proposed corrections with page/region evidence; do not interpret document meaning, guess unreadable text, certify authenticity or overwrite the source. - [Study Card Reviewer](https://agentscamp.com/agents/analytics/study-card-reviewer.md): Review a draft flashcard set against supplied notes and stated learning targets. Identify unsupported answers, compound questions, duplicate targets and source conflicts in a card-by-card report; do not draft a new set, schedule practice or infer learner ability. - [API Architect](https://agentscamp.com/agents/core-development/api-architect.md): Use this agent to design APIs — resource modeling, versioning, pagination, error contracts, REST vs GraphQL. Examples — designing a public API, reviewing an API spec, planning a breaking change. - [Backend Developer](https://agentscamp.com/agents/core-development/backend-developer.md): Use this agent to build server-side features — endpoints, business logic, data access, background jobs. Examples — a new REST/GraphQL endpoint, a queue worker, a database integration. - [Database Architect](https://agentscamp.com/agents/core-development/database-architect.md): Use this agent to design data models and storage strategy from access patterns — schema design, normalization vs deliberate denormalization, relational vs document vs key-value vs wide-column vs graph selection, indexing, partitioning/sharding, transaction boundaries, and consistency models. Examples — modeling a new feature's schema, choosing a database for a write-heavy event workload, reviewing a schema for missing indexes or scaling cliffs, planning how to shard a table that no longer fits one node. - [Frontend Developer](https://agentscamp.com/agents/core-development/frontend-developer.md): Use this agent to build UI — responsive layouts, components, accessibility, and design-system work. Examples — implementing a Figma design, fixing a11y issues, building a reusable component. - [GraphQL Architect](https://agentscamp.com/agents/core-development/graphql-architect.md): Use this agent to design GraphQL schemas and resolvers — types, nullability, connections, dataloaders, federation, depth/complexity limits. Examples — designing a new schema from requirements, killing N+1 queries in resolvers, planning a deprecation, hardening a public graph. - [Mobile Developer](https://agentscamp.com/agents/core-development/mobile-developer.md): Use this agent to build cross-platform mobile apps with React Native + Expo — screens, navigation, native modules, and shipping via EAS. Examples — adding a tab-based navigation flow, fixing a janky FlatList, shipping a build to TestFlight with EAS. - [System Architect](https://agentscamp.com/agents/core-development/system-architect.md): Use this agent for high-level system design — service boundaries, data flow, scaling, trade-offs. Examples — designing a new system, evaluating a monolith-to-services split, a scalability review. - [Agent Tool Integration Engineer](https://agentscamp.com/agents/data-ai/agent-tool-integration-engineer.md): Use this agent to wire tools and function-calling into an agent loop reliably — clean tool schemas, errors fed back as observations, retries with limits, idempotency, and parallel calls. Examples — "connect our APIs as agent tools", "our agent calls tools wrong / ignores tool errors", "add function-calling with proper error recovery to our agent". - [Browser Agent Engineer](https://agentscamp.com/agents/data-ai/browser-agent-engineer.md): Use this agent to build, harden, or debug browser-automation agents — web tasks via Browser Use, Stagehand, Skyvern, or Playwright-based stacks. Examples: automate a portal workflow, make a flaky browser agent reliable, add verification and guardrails to web automation, choose between vision and DOM grounding. - [Data Engineer](https://agentscamp.com/agents/data-ai/data-engineer.md): Use this agent to build and maintain data pipelines — ingestion, ELT/ETL, warehouse modeling, orchestration, and data-quality tests. Examples — building an idempotent ingestion job, modeling a fact/dimension table in dbt, writing a safe backfill for a changed schema. - [Data Scientist](https://agentscamp.com/agents/data-ai/data-scientist.md): Use this agent for data analysis — exploration, statistics, SQL, and clear findings. Examples — analyzing a dataset, writing an analytical SQL query, summarizing experiment results. - [Finetuning Engineer](https://agentscamp.com/agents/data-ai/finetuning-engineer.md): Use this agent to fine-tune an open-weight model end to end — confirming fine-tuning is the right tool, preparing the dataset, choosing the method (LoRA/QLoRA vs. full), running training, and proving the result beats the prompted baseline on a held-out eval set. Examples — "fine-tune a small model to match our support tone and answer format", "we have 800 labeled examples — LoRA-tune and show it beats prompting", "our fine-tune overfits and forgot general ability — fix the data and run". - [LLM Cost Optimizer](https://agentscamp.com/agents/data-ai/llm-cost-optimizer.md): Use this agent to cut the cost and latency of an application's LLM API usage without losing quality — audit where the tokens and dollars go, then apply caching, model right-sizing, prompt trimming, batching, and budgets, proven against an eval bar. Examples — "our OpenAI bill tripled, find where the spend is and cut it", "this endpoint's p95 is 8s, bring it down", "right-size models per task and add prompt caching to our chat feature". - [LLM Evaluation Engineer](https://agentscamp.com/agents/data-ai/llm-evaluation-engineer.md): Use this agent to make an LLM feature's quality measurable — building the dataset, choosing metrics, setting a baseline, and turning evals into a CI gate so prompt and model changes are scored, not guessed. Examples — "we changed the prompt and don't know if it's better, set up evals", "add a regression gate for our extraction feature", "our RAG quality is drifting, build an eval suite". - [LLM Inference Engineer](https://agentscamp.com/agents/data-ai/llm-inference-engineer.md): Use this agent to serve and optimize self-hosted LLM inference — sizing GPUs, configuring a serving engine like vLLM (continuous batching, PagedAttention, tensor parallelism), applying quantization, and tuning throughput and tail latency against a cost and p95 budget. Examples — "serve Llama-3-70B at p95 under 2s on our GPUs", "our self-hosted model is slow and the GPUs sit half-idle — raise throughput", "quantize this model to fit one GPU without wrecking quality". - [LLM Integration Engineer](https://agentscamp.com/agents/data-ai/llm-integration-engineer.md): Use this agent to add an LLM feature to an application and make it production-grade — typed/structured output, streaming, provider fallback and retries, caching, and cost/latency controls. Examples — "add an AI summary endpoint to our app", "our LLM calls return unparseable JSON and break, make them reliable", "add streaming and a fallback provider to our chat feature". - [LLM Observability Engineer](https://agentscamp.com/agents/data-ai/llm-observability-engineer.md): Use this agent to make a production LLM app observable — tracing every step, scoring live traffic with online evals, and monitoring quality, cost, and latency — so you can debug agent runs and catch regressions in production. Examples — "add tracing to our RAG/agent so we can debug bad answers", "set up online evals and cost/latency dashboards", "production quality is slipping and we're flying blind". - [ML Engineer](https://agentscamp.com/agents/data-ai/ml-engineer.md): Use this agent for production ML — pipelines, training, serving, evaluation, and MLOps. Examples — building a training pipeline, deploying a model, setting up evaluation. - [Postgres Migration Engineer](https://agentscamp.com/agents/data-ai/postgres-migration-engineer.md): Use this agent to plan and execute a zero-downtime Postgres schema migration — decomposing a breaking change into expand-contract steps, writing batched backfills, building indexes CONCURRENTLY, validating constraints online, and keeping every step reversible with the project's migration tooling. Examples — "add a NOT NULL column to a 200M-row table without downtime", "rename a column safely across a rolling deploy", "split this risky migration into reversible expand/contract steps". - [Prompt Engineer](https://agentscamp.com/agents/data-ai/prompt-engineer.md): Use this agent to design and iterate the prompts behind an LLM-powered product feature — instructions, few-shot examples, tool schemas, and the evals that prove a change actually helped. Examples — "this classification prompt is flaky, make it reliable", "design the system prompt and function schema for our support agent", "our extraction prompt regressed after I tweaked it, set up evals so this stops happening". - [Rag Pipeline Engineer](https://agentscamp.com/agents/data-ai/rag-pipeline-engineer.md): Use this agent to design, build, and harden a production retrieval-augmented generation (RAG) pipeline end to end — ingestion, chunking, embeddings, indexing, retrieval, reranking, and grounded generation — with evals that prove each stage works. Examples — "stand up RAG over our docs", "our RAG hallucinates and misses obvious answers, fix the pipeline", "take our prototype RAG to production with evals and citations". - [Retrieval Engineer](https://agentscamp.com/agents/data-ai/retrieval-engineer.md): Use this agent to raise the retrieval quality of a search or RAG system — recall and precision, hybrid (dense + sparse) search, reranking, query transformation, and metadata filtering — measured against a labeled eval set. Examples — "our RAG retrieves irrelevant chunks, fix recall", "add hybrid search and reranking and prove it helps", "queries with acronyms/IDs return nothing, fix it". - [Vector Search Engineer](https://agentscamp.com/agents/data-ai/vector-search-engineer.md): Use this agent to design, build, and tune the vector-database layer of a search or RAG system — schema and index design (HNSW/IVF + quantization), metadata/payload filtering, hybrid (dense + sparse) search, and ingestion/upsert pipelines — sized to a real latency, recall, and cost budget. Examples — "set up pgvector for our docs with HNSW and filtered search", "our Qdrant queries are slow and recall dropped after quantization", "add metadata filtering so search only returns the current tenant's documents". - [Voice Agent Engineer](https://agentscamp.com/agents/data-ai/voice-agent-engineer.md): Use this agent to build or fix a real-time voice agent — the streaming STT → LLM → TTS pipeline, conversational (mouth-to-ear) latency, turn-taking, barge-in/interruptions, and per-stage provider selection. Examples — "our voice bot feels laggy and talks over people, fix the turn-taking and latency", "build a phone agent that transcribes, answers with our LLM, and speaks back", "get our voice agent's response time under a second". - [Design Systems Librarian](https://agentscamp.com/agents/design/design-systems-librarian.md): Use this agent to keep a design system's tokens, components, and documentation consistent with each other — finding hardcoded colors, spacing, and radii that should be tokens, components with no docs and docs describing variants that no longer exist, naming drift between token names, component names, and design-tool variables, and stale documentation — then reporting the drift with severity and a prioritized fix list. Examples — 'audit our design system for hardcoded values', 'which components are undocumented or documented wrong?', 'our token names and the Figma variables have diverged, show me where'. - [Image Description Reviewer](https://agentscamp.com/agents/design/image-description-reviewer.md): Review still-image text alternatives against page purpose, verified visual evidence and source data; flag missing meaning, invented detail and chart-description conflicts without claiming conformance. - [Storyboard Continuity Reviewer](https://agentscamp.com/agents/design/storyboard-continuity-reviewer.md): Review storyboard frames or shot descriptions against an approved script and supplied continuity constraints. Flag omitted beats, changed actions, prop/character drift and timing assumptions in a shot-level report; do not rewrite the approved script, generate images or grant production approval. - [CLI Tooling Engineer](https://agentscamp.com/agents/developer-tools/cli-tooling-engineer.md): Use this agent to design or build a command-line tool — subcommand and flag layout, --help and error UX, exit codes, --json/machine output, config precedence, stdin/stdout/stderr and pipe behavior, TTY/color/NO_COLOR detection, and CLI testing. Examples — "design the command and flag surface for our new deploy CLI", "this tool prints errors to stdout and returns 0 on failure — fix its ergonomics", "make our command pipe-friendly and add a --json mode for CI". - [Dependency Manager](https://agentscamp.com/agents/developer-tools/dependency-manager.md): Use this agent to upgrade project dependencies safely — batching low-risk bumps apart from breaking majors and verifying each step. Examples — clearing months of stale packages, taking a single major version with migration notes, resolving a peer-dependency conflict. - [Documentation Engineer](https://agentscamp.com/agents/developer-tools/documentation-engineer.md): Use this agent to write and maintain technical docs that stay true to the code — READMEs, how-to guides, API references, and runbooks. Examples — updating a stale README after a refactor, documenting a new public API from its signatures, writing an on-call runbook for a service. - [Git Github Expert](https://agentscamp.com/agents/developer-tools/git-github-expert.md): Use this agent for Git and GitHub workflows — rebases, conflict resolution, history surgery, PRs, and Actions. Examples — resolving a messy merge, rewriting history safely, fixing a workflow file. - [MCP Server Engineer](https://agentscamp.com/agents/developer-tools/mcp-server-engineer.md): Use this agent to build, harden, or productionize a Model Context Protocol (MCP) server — designing tools/resources/prompts, choosing stdio vs. Streamable HTTP, taking a server remote with OAuth and stateless scaling, and testing it with the MCP Inspector. Examples — "wrap our internal API as an MCP server with three tools", "take our stdio server remote so the team can share it", "our tools confuse the model — fix the names, schemas, and descriptions". - [Refactoring Specialist](https://agentscamp.com/agents/developer-tools/refactoring-specialist.md): Use this agent to safely restructure code without changing behavior — extracting, renaming, decoupling. Examples — breaking up a god object, removing duplication, improving testability. - [CI/CD Engineer](https://agentscamp.com/agents/infrastructure-devops/ci-cd-engineer.md): Use this agent to design, speed up, and harden CI/CD pipelines on any provider (GitHub Actions, GitLab CI, CircleCI, Buildkite). Examples — setting up a build→test→deploy pipeline from scratch, cutting a 25-minute CI run down with caching and matrix parallelism, adding a canary or blue-green deploy with automatic rollback, or reviewing a workflow for leaked secrets, over-broad tokens, and unpinned third-party actions. - [Cloud Architect](https://agentscamp.com/agents/infrastructure-devops/cloud-architect.md): Use this agent to design a cloud architecture on AWS, GCP, or Azure — compute, networking, data stores, IAM, and cost trade-offs. Examples — choosing serverless vs containers for a new service, designing a multi-account network boundary, picking a database and estimating its monthly cost. - [DevOps Engineer](https://agentscamp.com/agents/infrastructure-devops/devops-engineer.md): Use this agent for CI/CD, infrastructure, and automation. Examples — writing a CI pipeline, containerizing an app, infrastructure-as-code changes. - [Incident Responder](https://agentscamp.com/agents/infrastructure-devops/incident-responder.md): Use this agent during a live production incident to restore service fast and learn from it — triage and severity, mitigation-first action (roll back, fail over, shed load), change correlation, status updates, and the blameless postmortem. Examples — an alert just fired and the API is 5xx-ing, a deploy broke checkout and you need to decide rollback vs. forward-fix, latency is climbing and the pager is going off, or you're writing the postmortem the morning after. - [Kubernetes Specialist](https://agentscamp.com/agents/infrastructure-devops/kubernetes-specialist.md): Use this agent for Kubernetes — manifests, Helm, troubleshooting, scaling, and resource tuning. Examples — debugging a CrashLoopBackOff, writing a Deployment, tuning requests/limits. - [SRE Engineer](https://agentscamp.com/agents/infrastructure-devops/sre-engineer.md): Use this agent to make reliability measurable: SLIs/SLOs and error budgets, observability, symptom-based alerting, incident response, and capacity. Examples — defining an SLO for a checkout API, fixing a noisy pager, writing a blameless postmortem. - [Terraform Specialist](https://agentscamp.com/agents/infrastructure-devops/terraform-specialist.md): Use this agent for Terraform and infrastructure-as-code — module design, remote state, plan/apply safety, drift, and provider pinning. Examples — reviewing a plan for destroys before apply, designing a reusable module, resolving state drift after a console change. - [C# Pro](https://agentscamp.com/agents/language-specialists/csharp-pro.md): Use this agent for modern C#/.NET 8+ — records, pattern matching, nullable reference types, correct async/await, LINQ, Span, and source generators — plus ASP.NET Core and EF Core. Examples — building a minimal-API service, fixing an EF Core N+1 or tracking leak, hunting a deadlock from sync-over-async, or turning on nullable reference types across a project. - [Golang Pro](https://agentscamp.com/agents/language-specialists/golang-pro.md): Use this agent for idiomatic Go — concurrency, errors, small interfaces, stdlib-first design, and profiling. Examples — fixing a goroutine leak, designing a context-aware API, profiling a hot path with pprof. - [Java Pro](https://agentscamp.com/agents/language-specialists/java-pro.md): Use this agent for idiomatic, modern Java (17/21+) — records, sealed types, pattern matching, virtual threads and structured concurrency, the Streams API, and JVM/GC performance. Examples — modernizing a legacy POJO-and-thread-pool service to records and virtual threads, diagnosing a GC pause or allocation hotspot, reviewing concurrency correctness, or fixing a Spring Boot service that blocks the wrong threads. - [Kotlin Pro](https://agentscamp.com/agents/language-specialists/kotlin-pro.md): Use this agent for idiomatic Kotlin — null safety, coroutines and structured concurrency, Flow, sealed classes with exhaustive when, data classes, and extension functions — on Android and the JVM. Examples — fixing a coroutine leak, replacing callbacks with Flow, removing !! null-safety holes. - [Php Pro](https://agentscamp.com/agents/language-specialists/php-pro.md): Use this agent for idiomatic, modern PHP 8.3+ — strict types, enums, readonly and promoted properties, Composer/PSR-4 autoloading, and safe PDO data access. Examples — modernizing a PHP 5-era class, killing an ORM N+1, hardening a query against SQL injection. - [Python Pro](https://agentscamp.com/agents/language-specialists/python-pro.md): Use this agent for idiomatic, performant Python — typing, async, packaging, and stdlib mastery. Examples — refactoring to idiomatic Python, async I/O, packaging a library. - [React Specialist](https://agentscamp.com/agents/language-specialists/react-specialist.md): Use this agent for React architecture — hooks, state, performance, Server Components, and patterns. Examples — fixing re-render issues, designing component state, adopting RSC. - [Rust Pro](https://agentscamp.com/agents/language-specialists/rust-pro.md): Use this agent for idiomatic Rust — ownership, lifetimes, error handling, traits, async with tokio, and the cargo toolchain. Examples — fixing borrow-checker errors, designing a trait API, making async code compile cleanly under tokio. - [SQL Pro](https://agentscamp.com/agents/language-specialists/sql-pro.md): Use this agent for SQL itself — correct joins and window functions, indexing, EXPLAIN plans, schema design, and safe migrations on Postgres/MySQL. Examples — making a slow query fast, designing a normalized schema, writing a reversible migration. - [Swift Pro](https://agentscamp.com/agents/language-specialists/swift-pro.md): Use this agent for modern Swift 6 — value semantics, optionals done right, async/await and actors, Sendable/data-race safety, and idiomatic SwiftUI. Examples — fixing a data race under strict concurrency, untangling force-unwrap crashes, making a SwiftUI list scroll smoothly. - [Typescript Pro](https://agentscamp.com/agents/language-specialists/typescript-pro.md): Use this agent for advanced TypeScript — generics, type-level programming, strictness, and inference. Examples — typing a tricky API, fixing type errors, designing a type-safe library surface. - [Caption Reviewer](https://agentscamp.com/agents/marketing/caption-reviewer.md): Review supplied caption text, timing and human listening notes against a named media version. Flag uncertain words, speaker/sound omissions and timing conflicts in an issue ledger; never claim to hear inaccessible audio, rewrite the source media or certify accessibility compliance. - [Content Editor](https://agentscamp.com/agents/marketing/content-editor.md): Use this agent to edit marketing and editorial drafts (blog posts, landing pages, emails, social copy, newsletters) for clarity and brand voice while marking every unsourced claim, statistic, and superlative with a [CITATION NEEDED] or [VERIFY] tag and a one-line reason, never inventing a source, and returning the edited draft with a change log. Examples — 'edit this post before it goes to the client', 'tighten this launch email and tell me which numbers I need to back up', 'check this page copy against brand-voice.md and mark anything we cannot prove'. - [Localization Copy Reviewer](https://agentscamp.com/agents/marketing/localization-copy-reviewer.md): Review paired source and translated marketing or help-document text for a specified locale against supplied terminology and style rules: flag lost meaning, negation, conditions, numbers, protected terms and ambiguous segments, with evidence-linked proposals for a human language reviewer. - [Speech Cleanup Reviewer](https://agentscamp.com/agents/marketing/speech-cleanup-reviewer.md): Review original-versus-processed speech listening notes and a timed cleanup sheet for a supplied recording. Flag lost words, altered pronunciation, missing useful sound and unsupported keep decisions; do not claim to listen through a text-only tool, process audio or select a final master without a listener. - [Agent Architect](https://agentscamp.com/agents/meta-orchestration/agent-architect.md): Use this agent to design a new Claude Code subagent or review an existing one — scoping, description, toolset, model, and output contract. Examples — "design an agent that triages flaky tests", "review my code-reviewer agent for scope creep", "why won't Claude auto-delegate to my agent?". - [Agent Reliability Reviewer](https://agentscamp.com/agents/meta-orchestration/agent-reliability-reviewer.md): Use this agent to make an AI agent production-ready — reviewing its loops, cost controls, error handling, tool use, human-in-the-loop gates, checkpointing, and observability, then reporting concrete failure modes and fixes. Examples — "is our agent safe to ship?", "our agent loops forever / burns tokens, harden it", "add guardrails and recovery before we put this agent in front of users". - [Context Engineer](https://agentscamp.com/agents/meta-orchestration/context-engineer.md): Use this agent to engineer what an LLM agent carries in its context window — deciding what to include vs exclude vs retrieve on demand, designing project/agent memory (CLAUDE.md), compacting growing history, and allocating the token budget across system prompt, memory, retrieved docs, tool results, and conversation. Examples — "my agent forgets the schema we agreed on three turns ago", "the agent gets dumber and more inconsistent as the chat grows", "we're burning 60k tokens of tool output every turn", "what should this support agent always know vs look up?". - [Eval Driven Developer](https://agentscamp.com/agents/meta-orchestration/eval-driven-developer.md): Use this agent to drive AI feature development with evals the way TDD drives code with tests — define success criteria and a representative eval set BEFORE iterating on prompts/models, then optimize against measured scores instead of vibes. Examples — "make the summarizer better" (turn it into measurable criteria first), "our prompt change keeps regressing quality, set up a loop that catches it", "add an eval gate to CI so a model swap can't silently degrade output", "we tweak prompts and pray — give us a baseline and a change-by-change scoreboard". - [Workflow Orchestrator](https://agentscamp.com/agents/meta-orchestration/workflow-orchestrator.md): Use this agent to break large tasks into coordinated multi-step plans and delegate to other agents. Examples — planning a multi-file refactor, orchestrating a migration, decomposing an epic. - [Contract Obligation Reviewer](https://agentscamp.com/agents/product/contract-obligation-reviewer.md): Compare a prepared vendor-contract obligation register with supplied agreement and amendment text: flag missing qualifying language, unsupported dates or parties, unresolved cross-references, and unproven supersession. Return a source-linked review packet for qualified counsel; do not give legal conclusions or approve terms. - [Interview Scorecard Reviewer](https://agentscamp.com/agents/product/interview-scorecard-reviewer.md): Review a draft interview scorecard against supplied job tasks: flag criteria lacking job evidence, vague behavioral anchors, inconsistent question coverage, and unsupported weights before interviews. Review the rubric only; never score, rank, infer traits about, or select candidates. - [Procedure Exception Reviewer](https://agentscamp.com/agents/product/procedure-exception-reviewer.md): Review a drafted workplace SOP against captured walkthroughs and supplied exception cases: identify absent branches, unmet prerequisites, unclear stop conditions, and unsupported recovery steps without executing or editing the procedure. - [Project Handoff Auditor](https://agentscamp.com/agents/product/project-handoff-auditor.md): Use this agent to review a prepared human-to-human project handoff against its local source files: find missing deliverables, unsupported completion claims, unclear ownership, stale or conflicting decisions, and questions the receiving teammate cannot resolve from the packet. Report evidence and fixes without editing the packet. - [Support Answer Auditor](https://agentscamp.com/agents/product/support-answer-auditor.md): Review prepared customer-support answer cards against supplied policy versions and applicability rules: flag unsupported promises, omitted eligibility conditions, contradictory answers, and missing escalation boundaries before the library is approved. - [Survey Design Reviewer](https://agentscamp.com/agents/product/survey-design-reviewer.md): Use this agent to review a draft customer survey before collection: check leading wording, double-barreled questions, missing response options, inconsistent scales, unsupported recall periods, and whether each question can answer the stated research objective. Return an annotated review and proposed rewrites without changing the survey. - [Technical Cofounder](https://agentscamp.com/agents/product/technical-cofounder.md): Use this agent when a non-technical founder needs an app built by an AI app builder or coding agent reviewed for the problems that hurt founders: missing login and permission checks, secrets committed to the code, customer data visible to the wrong users, surprise cloud and API bills, vendor lock-in, no backups, and no tests. It explains each finding in plain language with a severity and the question to ask an engineer. Examples — 'look over what Lovable built before I launch', 'is this Supabase app safe for real customer data', 'what will bite me if I ship this Claude Code project'. - [Accessibility Auditor](https://agentscamp.com/agents/quality-security/accessibility-auditor.md): Use this agent to audit web UI against WCAG 2.2 AA — semantics, keyboard, ARIA, contrast, forms, and motion. Examples — auditing a new component for keyboard traps, checking a form for accessible errors, running a pre-ship a11y pass on a page. - [Code Reviewer](https://agentscamp.com/agents/quality-security/code-reviewer.md): Use this agent to review code changes for correctness, security, and maintainability before merging. Examples — reviewing a PR diff, auditing a new module, checking a refactor for regressions. - [Debugger](https://agentscamp.com/agents/quality-security/debugger.md): Use this agent to diagnose failing tests, runtime errors, or unexpected behavior by forming and testing hypotheses. Examples — a stack trace to root-cause, a flaky test, a "works locally but not in CI" bug. - [Performance Engineer](https://agentscamp.com/agents/quality-security/performance-engineer.md): Use this agent to profile and optimize performance — latency, throughput, memory, bundle size. Examples — a slow endpoint, an N+1 query, a heavy render, a large JS bundle. - [Prompt Injection Auditor](https://agentscamp.com/agents/quality-security/prompt-injection-auditor.md): Use this agent to audit an LLM app or agent for prompt-injection exposure — mapping where untrusted content enters the model's context (user, RAG, tools, web), assessing the blast radius if an injection succeeds, probing with adversarial inputs, and recommending architectural mitigations. Examples — "audit our RAG agent for indirect prompt injection", "what's the blast radius if our agent gets injected — which tools and credentials are exposed?", "review our LLM app's trust boundaries and tell us what to fix". - [QA Automation Engineer](https://agentscamp.com/agents/quality-security/qa-automation-engineer.md): Use this agent for end-to-end and UI test automation — building flake-resistant Playwright/Cypress suites, stabilizing flaky browser tests, structuring page objects and fixtures, and reviewing E2E suites. Examples — adding E2E coverage for a checkout or signup flow, killing a test that fails 1-in-5 in CI, choosing a framework and folder structure, replacing sleeps with web-first waits, or auditing a suite that's slow and brittle. - [Security Auditor](https://agentscamp.com/agents/quality-security/security-auditor.md): Use this agent to find security vulnerabilities — injection, auth flaws, secrets, unsafe deserialization, dependency risks. Examples — auditing an API surface, reviewing auth code, pre-release security pass. - [Test Engineer](https://agentscamp.com/agents/quality-security/test-engineer.md): Use this agent to write and improve automated tests — unit, integration, and edge cases. Examples — adding coverage to an untested module, writing regression tests for a bug, designing a test plan. - [RFP Completeness Reviewer](https://agentscamp.com/agents/sales/rfp-completeness-reviewer.md): Review a prepared RFP submission against supplied requirements, addenda and attachment rules: find unanswered subparts, omitted documents, unproven exceptions, contradictory IDs and submission-rule gaps. Return an evidence-linked completeness review without drafting sales claims or submitting a bid. - [Sales Engineer](https://agentscamp.com/agents/sales/sales-engineer.md): Use this agent to review the technical asks a prospect has put in front of a deal — security questionnaires, integration and API requirements, SSO and provisioning, SLA and uptime demands, data residency and retention terms, custom feature requests — and separate what the product does today from what needs engineering, with evidence for each verdict from the product's own docs and code. It writes the answer language a rep can safely send, and flags the commitments nobody should make without Engineering, Security, Legal, or Finance signing off. Examples — 'they want SSO, SCIM and EU data residency, what can we say yes to', 'review this integration requirements doc before Thursday's technical call', 'they are asking for a 99.99% SLA and two-hour breach notification, tell me what we cannot commit to'. ## Skills Packaged SKILL.md capabilities that extend Claude with on-demand expertise. - [Analysis Memo Writer](https://agentscamp.com/skills/analytics/analysis-memo-writer.md): Turn findings and figures you supply into a stakeholder writeup with a fixed spine: headline finding, what the number is and what it is not, method in three lines, a mandatory caveats and limitations section, what would change the conclusion, and one recommended next step. Never invents a figure and never upgrades a correlation into a cause. Use when the analysis is done and has to survive being read by someone who was not in the query. - [Chart Chooser](https://agentscamp.com/skills/analytics/chart-chooser.md): Pick the chart for a stated question and data shape and defend the choice: the recommendation, the reasoning, the alternatives rejected and why each fails this question, encoding rules for axis baseline, sorting, color, and labels, and generated code in the plotting library you name. Use when you know what you want the chart to say and need the one form that says it, not a gallery. - [Dataset First Look](https://agentscamp.com/skills/analytics/dataset-first-look.md): Turn a pasted CSV header with sample rows, a pasted table, or an attached data file into a fixed plain-language profile: shape, column inventory with inferred types, null and blank patterns, cardinality, suspicious columns, duplicate-key risk, outliers worth a look, and the questions to settle before analyzing. Use when a dataset just landed and you need to know what you are holding before you write a query. - [Entity Match Candidate Ledger](https://agentscamp.com/skills/analytics/entity-match-candidate-ledger.md): Build a field-evidence ledger for proposed duplicate or cross-source record pairs, preserving source keys, original values and match/different/unresolved owner decisions without merging records. - [Spreadsheet Formula Auditor](https://agentscamp.com/skills/analytics/spreadsheet-formula-auditor.md): Audit pasted spreadsheet formulas or a described model for the errors that survive review: hardcoded values buried inside formulas, ranges that drift or truncate, one cell in a row that does not match its neighbors, circular references, sign errors, IFERROR masking a real failure, and volatile functions, returned as a fix list ordered by how much money the error moves. Use before a model that carries a real decision leaves your hands. - [SQL Explainer](https://agentscamp.com/skills/analytics/sql-explainer.md): Walk through a pasted SQL query in execution order in plain language: what each step does, the grain of the result, where a join can fan rows out, which filters silently drop rows, and a ranked list of what could be wrong. Comprehension only, not performance tuning. Use when you inherited a query, are reviewing one before trusting its numbers, or have to explain it to someone who does not read SQL. - [API Deprecation Planner](https://agentscamp.com/skills/api/api-deprecation-planner.md): Plan the retirement of an API endpoint, field, event, tool, or version without surprising active consumers. Use when replacing an interface, removing legacy behavior, publishing a sunset, migrating internal or external clients, or deciding whether observed traffic is safe to turn off. - [API Error Contract Designer](https://agentscamp.com/skills/api/api-error-contract-designer.md): Design or normalize an API's error contract so clients get stable machine-readable codes, safe human messages, field-level validation details, correlation IDs, and consistent HTTP semantics. Use when adding endpoints, replacing ad hoc error strings, documenting SDK behavior, or fixing clients that branch on fragile message text. - [CORS Configurator](https://agentscamp.com/skills/api/cors-configurator.md): Diagnose and fix a failing cross-origin browser request — read the exact console error, work out whether it's a simple or preflighted request, and set the minimal correct Access-Control-* response headers (including the wildcard-with-credentials trap and the OPTIONS preflight) on the server. Use when the browser blocks a fetch/XHR with a CORS error but the API itself works from curl or Postman. - [GraphQL Schema Designer](https://agentscamp.com/skills/api/graphql-schema-designer.md): Design a clean, evolvable GraphQL schema (SDL) that won't paint you into a corner — model the graph around domain types and their relationships rather than as RPC-over-GraphQL, set nullability deliberately, standardize lists with Relay connections, plan DataLoader batching for per-parent fields, and evolve by adding + @deprecated instead of versioning. Use when designing a new GraphQL API, reviewing an SDL, or migrating REST endpoints to a graph. - [Idempotency Designer](https://agentscamp.com/skills/api/idempotency-designer.md): Make unsafe, retryable API operations idempotent so a client retry or a network hiccup can't double-charge, double-create, or double-send — design a client-supplied idempotency key, an atomic store-and-check (unique constraint or conditional write), in-flight conflict handling, and a retention policy. Use when a POST/mutation can be retried (payments, order creation, sends, webhooks), or when duplicate side effects have already shown up in production. - [LLM Output Schema Generator](https://agentscamp.com/skills/api/llm-output-schema-generator.md): Turn an example of the data you want from an LLM into a precise, validated output schema (Pydantic / Zod / JSON Schema) and wire it into structured-output calls. Use when adding typed LLM output, replacing brittle JSON parsing, or designing an extraction shape. - [MCP Server Scaffolder](https://agentscamp.com/skills/api/mcp-server-scaffolder.md): Scaffold a new Model Context Protocol (MCP) server from a description — pick the SDK and transport, generate a typed first tool with a strict schema, and wire up MCP Inspector testing and the client-registration command. Use when starting a new MCP server and you want a correct, runnable skeleton instead of copying a README. - [Pagination Designer](https://agentscamp.com/skills/api/pagination-designer.md): Design correct, scalable pagination (plus the filtering and sorting that ride with it) for a list endpoint — pick cursor (keyset) vs offset and justify it, define an opaque cursor with a unique tiebreaker so no row is skipped or repeated, return a consistent envelope, bound page size, and name the indexes the sort actually needs. Use when adding a list endpoint, when OFFSET pagination crawls on a large table, or when clients see duplicate or missing rows while paging. - [Provider Fallback Wrapper](https://agentscamp.com/skills/api/provider-fallback-wrapper.md): Wrap LLM calls so a provider outage, rate limit, or timeout degrades gracefully — with multi-provider fallback, bounded retries with backoff, and timeouts. Use when an app depends on a single model/provider and needs production resilience. - [Rate Limiter Designer](https://agentscamp.com/skills/api/rate-limiter-designer.md): Design and implement API rate limiting that actually holds under load — pick the algorithm (token bucket vs sliding-window-counter vs fixed window) and justify it, choose the limiting key and per-tier limits, use cross-instance atomic storage, and return standard 429 signals. Use when protecting an API from abuse or scrapers, enforcing per-tier quotas, or replacing an in-memory limiter that breaks behind multiple replicas. - [Tool Definition Generator](https://agentscamp.com/skills/api/tool-definition-generator.md): Generate clean function/tool schemas for an LLM agent from existing code or a spec — accurate JSON Schema, model-facing descriptions, honest required fields, and enums that make invalid calls impossible. Use when wiring functions into an agent's tool-calling loop. - [Webhook Handler Scaffolder](https://agentscamp.com/skills/api/webhook-handler-scaffolder.md): Scaffold a robust inbound webhook handler that verifies the signature on the raw body first, dedupes on the provider's event id, acknowledges fast, and processes asynchronously — the four things naive handlers get wrong. Use when wiring up events from a third party (Stripe, GitHub, Shopify, Slack, Twilio), when a provider keeps retrying because your endpoint times out or 500s, or when duplicate events are double-charging or double-creating records. - [Agent Trajectory Evaluator](https://agentscamp.com/skills/data/agent-trajectory-evaluator.md): Evaluate a multi-step AI agent's whole run — tool calls, intermediate steps, and final result — not just final-answer correctness, so you can pinpoint WHERE it went wrong. Use when building or debugging a tool-using or multi-step agent, when final-answer-only evals can't explain failures, or when a prompt/model change quietly makes the agent less efficient or more error-prone even though the answer still looks right. - [Annotation Disagreement Ledger](https://agentscamp.com/skills/data/annotation-disagreement-ledger.md): Build an adjudication ledger from conflicting supervised text-label responses, preserving original reviewer labels, exact excerpts, task-rule versions and unresolved owner decisions. - [Annotation Task Spec Builder](https://agentscamp.com/skills/data/annotation-task-spec-builder.md): Specify the record and annotation-interface contract for an owner-defined supervised text-labeling task: approved label IDs, input text, model suggestions, human response fields and abstain handling. - [Chunking Strategy Optimizer](https://agentscamp.com/skills/data/chunking-strategy-optimizer.md): Find the chunking strategy and size that maximizes retrieval quality for a specific corpus, by sweeping configurations against a fixed eval set instead of guessing. Use when RAG answers miss obvious content, when standing up a new corpus, or when picking chunk size/overlap. - [Embedding Set Inspector](https://agentscamp.com/skills/data/embedding-set-inspector.md): Diagnose the health of an embedding set before blaming the retriever — checking normalization, dimensionality, near-duplicates, degenerate vectors, and corpus/query distribution mismatch. Use when retrieval quality is poor, after a re-embed, or before shipping a new index. - [Finetune Dataset Builder](https://agentscamp.com/skills/data/finetune-dataset-builder.md): Turn raw examples into a training-ready fine-tuning dataset — normalize to the trainer's chat/instruction format, deduplicate (including near-duplicates), strip PII, balance, validate the schema and token lengths, and carve a leak-free eval split. Use when you have raw examples and need a clean, formatted, split dataset before training. - [Graphrag Scaffolder](https://agentscamp.com/skills/data/graphrag-scaffolder.md): Stand up a GraphRAG experiment the disciplined way: audit whether your failed queries are actually connection-shaped, scope a minimal entity/relationship ontology, build extraction → graph → community-summary indexing on a corpus slice, and measure against vector-RAG baselines before committing. Use when multi-hop or whole-corpus questions keep failing plain RAG. - [Hallucination Evaluator](https://agentscamp.com/skills/data/hallucination-evaluator.md): Detect and measure ungroundedness in LLM and RAG outputs — claims the source doesn't support — by decomposing answers into atomic claims and checking each for entailment, so you can quantify faithfulness and gate on it instead of eyeballing it. Use when a RAG/LLM feature makes confident wrong claims, before shipping anything that must be factual, or to add a groundedness gate to evals/CI. - [LLM As Judge Scorer](https://agentscamp.com/skills/data/llm-as-judge-scorer.md): Design a reliable LLM-as-judge metric — a calibrated rubric, a clear scoring scale, and bias controls — and validate it against human labels before trusting it. Use when grading open-ended LLM output (summaries, answers, tone) that exact-match can't score. - [LLM Eval Suite Scaffolder](https://agentscamp.com/skills/data/llm-eval-suite-scaffolder.md): Stand up an evaluation suite for an LLM feature from scratch — a representative dataset, the right metrics, a baseline score, and a CI gate — using DeepEval, promptfoo, or RAGAS. Use when a feature has no evals, before tuning a prompt, or when adding an LLM feature to CI. - [Model Router Designer](https://agentscamp.com/skills/data/model-router-designer.md): Design a model router that sends each LLM request to the cheapest model that can handle it and escalates only the hard cases to the strongest — cutting cost and latency without tanking quality, gated by an eval set so the savings don't come from silently worse answers. Use when one expensive model serves all traffic (most of it easy), when LLM cost or latency is too high, or when balancing quality against spend across a range of request difficulty. - [Multimodal Document Extractor](https://agentscamp.com/skills/data/multimodal-document-extractor.md): Extract structured data from documents and images with a vision-language model — define the target schema, prompt the VLM to fill it from the page (invoices, forms, receipts, statements, IDs), and verify critical fields against the source. Use when you need reliable structured output from messy, varied, or scanned documents that defeat template-based OCR. - [Prompt Regression Tester](https://agentscamp.com/skills/data/prompt-regression-tester.md): Build a regression test harness for an LLM prompt so a prompt edit or model upgrade can't silently degrade quality — a fixed eval set, checkable assertions, and a diff against a committed baseline. Use when changing a production prompt, migrating model versions, or any time 'I tweaked the prompt' needs to be backed by evidence instead of eyeballing two outputs. - [Qlora Finetune Runner](https://agentscamp.com/skills/data/qlora-finetune-runner.md): Run a QLoRA (4-bit LoRA) fine-tune of an open-weight model from a prepared dataset — set up the config, train memory-efficiently (e.g. with Unsloth/PEFT), watch for overfitting, save the adapter, and run a quick eval against the prepared split. Use when you have a clean dataset and want to execute a parameter-efficient fine-tune on a single GPU. - [Research Evidence Extractor](https://agentscamp.com/skills/data/research-evidence-extractor.md): Extract a structured study evidence table from supplied paper text or readable research excerpts using a user-defined column schema: record study and source IDs, methods, population, outcomes, units, time points, source locations, and missing data. Use after papers have been selected and before synthesis; do not search for papers, decide eligibility, compute pooled effects, or draft conclusions. - [Semantic Cache Designer](https://agentscamp.com/skills/data/semantic-cache-designer.md): Design a semantic cache for LLM responses — serve a cached answer when a new query is similar enough to a past one — to cut cost and latency on repetitive traffic, with the similarity threshold calibrated on real query pairs and a cache key that prevents cross-user/model leaks. Use when an LLM app sees many near-duplicate prompts (FAQs, support, search), when token spend on repetitive queries is high, or when latency on common questions matters. - [SQL Optimizer](https://agentscamp.com/skills/data/sql-optimizer.md): Diagnose a slow SQL query from its execution plan and propose a verified optimization — finding the real bottleneck (sequential scan, missing or unused index, bad join order, app-side N+1) and measuring the fix before and after. Use when a query is slow and you need a fix backed by EXPLAIN ANALYZE, not a guess. - [Token Usage Profiler](https://agentscamp.com/skills/data/token-usage-profiler.md): Measure and attribute LLM token usage and cost across an app — input vs output tokens by feature, route, model, and tenant — then rank the waste and the specific lever to cut it. Use when LLM spend is high or climbing with no clear cause, before scaling a feature that calls a model, or when you need per-feature or per-tenant cost attribution for billing or budgets. - [Web Research Pipeline](https://agentscamp.com/skills/data/web-research-pipeline.md): Run a structured web-research pass on a question: plan the searches, find sources via search APIs, fetch and read the best ones, cross-check claims, and synthesize a cited answer — with source quality and disagreements surfaced honestly. Use for 'research X and tell me what's actually true' tasks that need more than one search and less than a day. - [Connection Pool Tuner](https://agentscamp.com/skills/database/connection-pool-tuner.md): Size and tune a database connection pool from the real constraint — the database's shared max_connections and its core count — so total connections (per-instance pool × instance count) stay safely under the cap and a too-large pool stops adding latency. Use when the app throws 'too many connections' or pool-acquire timeouts, when the DB is saturated by connection count, or when deploying to serverless. - [Deadlock Diagnoser](https://agentscamp.com/skills/database/deadlock-diagnoser.md): Diagnose a database deadlock from the engine's own deadlock report, reconstruct the lock cycle (A holds 1 wants 2, B holds 2 wants 1), name the root cause — almost always two code paths locking the same rows in different orders — and fix it with consistent lock ordering, shorter transactions, and a retry-the-victim safeguard. Use when the DB logs deadlock errors, when transactions intermittently fail under load, or when queries mysteriously block each other. - [Embedding Index Tuner](https://agentscamp.com/skills/database/embedding-index-tuner.md): Tune a vector index — HNSW graph parameters and quantization — to hit a recall target at the lowest latency and memory, by sweeping settings against a fixed query set instead of trusting defaults. Use when vector search is slow or memory-hungry, when recall dropped after enabling quantization, or when standing up an index and you need defensible parameters. - [Migration Writer](https://agentscamp.com/skills/database/migration-writer.md): Write a safe, reversible, zero-downtime database migration using expand-contract — add the new shape, backfill in batches, switch reads/writes, then drop the old — so every deploy stays compatible with the running app version. Use when adding or changing schema on a live system, renaming/dropping a column, adding NOT NULL or a foreign key on a large table, or when a migration risks locks, table rewrites, or an unrevertable step. - [Postgres Index Strategist](https://agentscamp.com/skills/database/postgres-index-strategist.md): Recommend the right Postgres index for a query or workload — choosing B-Tree vs. GIN vs. BRIN vs. partial/covering/expression, checking for redundant or unused indexes, and verifying the choice against the query plan. Use when a query needs an index, when deciding an index type for jsonb/array/full-text/time-series data, or when auditing an over-indexed table. - [Query Plan Analyzer](https://agentscamp.com/skills/database/query-plan-analyzer.md): Read a slow query's execution plan and turn it into a concrete fix — the exact index to add, the rewrite, or the ANALYZE to run — by getting the REAL plan with EXPLAIN ANALYZE (actual rows + timing, not estimates), finding the offending node, and confirming the fix removes it. Use when one specific query is slow and you need to know WHY, not just that it is. - [Safe Data Backfill Planner](https://agentscamp.com/skills/database/safe-data-backfill-planner.md): Plan and implement a resumable production data backfill with bounded batches, checkpoints, rate limits, correctness verification, observability, and a pause or rollback path. Use when populating a new column, repairing historical rows, re-keying records, migrating derived data, or updating millions of records without overwhelming the database or replicas. - [3D Asset Handoff Sheet](https://agentscamp.com/skills/design/3d-asset-handoff-sheet.md): Build a 3D asset handoff sheet from supplied export metadata, target-app technical reports and human viewer notes, preserving mesh/UV/material versions and untested checks. - [Component Spec Writer](https://agentscamp.com/skills/design/component-spec-writer.md): Turn a component screenshot or written description into a developer handoff spec covering anatomy, variants, states, a props table with types and defaults, behavior, responsive rules, edge cases, accessibility notes for an auditor to verify, and open questions, keeping what the artifact shows separate from what was inferred and turning every gap into a question instead of a plausible default. Use when a component is going to engineering and the design file is the only documentation that exists. - [Design Brief Writer](https://agentscamp.com/skills/design/design-brief-writer.md): Turn a messy design request you paste in (a Slack thread, an email chain, meeting notes, a ticket) into a one-page design brief: goal, audience, constraints, success criteria, scope and non-goals, references needed, open questions, and timeline placeholders, with every item the source did not state marked as an inference and no constraint or deadline invented. Use when a request has arrived in fragments and the brief has to exist before design work starts. - [Design Critique Checklist](https://agentscamp.com/skills/design/design-critique-checklist.md): Critique an attached screenshot or a described screen against a fixed seven-part checklist (visual hierarchy, spacing and alignment, typography, color and contrast as observed, empty, loading, and error states, consistency, and copy) and return findings in one set report format, each with a location, a severity, and a suggested fix, deferring every WCAG measurement to an accessibility audit. Use when you want a repeatable design review of a screen that reads the same from one round to the next. - [Design Token Extractor](https://agentscamp.com/skills/design/design-token-extractor.md): Read a screenshot, a pasted stylesheet, or a written component description and produce a design token set as W3C DTCG-style JSON, with colors, a type scale, spacing, radii, and shadows grouped and named, followed by a notes list saying which values were read exactly, snapped to a scale, or estimated by eye. Use when a design exists only as pixels or ad-hoc CSS and you need a named token set to start from. - [Image Description Draft](https://agentscamp.com/skills/design/image-description-draft.md): Draft purpose-specific still-image text alternatives from supplied verified visual observations and page context, separating short alt text from complex-image long-description needs. - [Storyboard Shot Brief](https://agentscamp.com/skills/design/storyboard-shot-brief.md): Convert an approved short script into a shot brief with stable beat IDs, action, dialogue, composition, continuity constraints and proposed duration. Use before storyboard image generation; do not write a new script, create final art or approve a production. - [UX Copy Reviewer](https://agentscamp.com/skills/design/ux-copy-reviewer.md): Review a set of interface strings you paste in (buttons, labels, empty states, errors, confirmations, tooltips) as one set rather than one at a time, checking clarity, term consistency across screens, error and empty-state quality, whether each action label matches what happens, and length against any limits you supply, and returning a verdict and a rewrite per string without inventing a product fact the strings do not contain. Use when microcopy has accumulated across a product and nobody has read it end to end. - [Adr Writer](https://agentscamp.com/skills/docs/adr-writer.md): Write an Architecture Decision Record capturing a decision the user describes, in Michael Nygard ADR format (Status, Context, Decision, Consequences) with an added Considered Alternatives section. Use when recording a significant architectural or technology choice. - [Architecture Diagram Generator](https://agentscamp.com/skills/docs/architecture-diagram-generator.md): Generate accurate architecture diagrams as Mermaid — straight from the codebase, not from imagination — by first choosing which view answers the question (container/component, sequence, ER, or state) and then reading the real entry points, module boundaries, service calls, and schema. Use when onboarding to an unfamiliar repo, documenting a system, or visualizing one complex flow. - [Code Example Verifier](https://agentscamp.com/skills/docs/code-example-verifier.md): Verify documentation code examples against the current repository and toolchain, then fix snippets, imports, commands, and expected output without changing the documented intent. Use when docs examples may be stale, an SDK or API changed, users report copy-paste failures, or before publishing tutorials, READMEs, migration guides, and release documentation. - [Contract Obligation Register](https://agentscamp.com/skills/docs/contract-obligation-register.md): Extract a draft vendor-contract obligation register from supplied agreement text with parties, duties, triggers, timing wording, conditions, exceptions, cross-references and exact section evidence. Use to prepare a qualified reviewer’s packet; do not interpret enforceability, choose controlling terms, or invent deadlines. - [Decision Log Reconciler](https://agentscamp.com/skills/docs/decision-log-reconciler.md): Reconcile an existing business-project decision log with supplied dated notes and change records: connect decisions to evidence, preserve explicit supersession links, separate accepted choices from proposals, and list contradictions awaiting an owner. Use when project records disagree about what was decided; do not write an architectural ADR or create meeting minutes. - [Exit Ticket Builder](https://agentscamp.com/skills/docs/exit-ticket-builder.md): Draft a short exit ticket from educator-approved learning objectives and taught material, mapping each question to an objective with an answer key and evidence. Use for low-stakes instructional feedback; do not grade student submissions, diagnose needs or invent curriculum standards. - [OCR Correction Ledger](https://agentscamp.com/skills/docs/ocr-correction-ledger.md): Build a proposed OCR correction ledger from a fixed OCR text copy and readable source-page images or human-verified image notes, preserving original spans, Unicode offsets, replacements, locators and unresolved status. Use before a reviewer creates a corrected copy; do not extract business conclusions or alter original scans. - [Onboarding Guide Writer](https://agentscamp.com/skills/docs/onboarding-guide-writer.md): Write a developer onboarding guide that gets a new contributor from clone to first merged change fast — a verified golden path, a quick architecture map, the real workflow conventions, and the gotchas that live only in senior engineers' heads. Use when a repo has no onboarding doc, when new hires keep asking the same setup questions, or when the README is a marketing page instead of a contributor guide. - [OpenAPI Doc Writer](https://agentscamp.com/skills/docs/openapi-doc-writer.md): Produce and maintain OpenAPI documentation for an HTTP API. Use when documenting endpoints, request/response schemas, or generating API reference docs. - [Process to SOP](https://agentscamp.com/skills/docs/process-to-sop.md): Convert supplied workplace walkthrough notes or readable process captures into one draft SOP with sourced steps, explicit inputs and outputs, observed branches, stop conditions, and an owner review queue. Use after a real process has been captured; do not invent policy or write an incident runbook. - [Readme Generator](https://agentscamp.com/skills/docs/readme-generator.md): Generate or refresh a project README grounded in the actual repository. Use when a project has no README, a stale one, or you want install/usage/scripts/structure sections that match the real code. - [Runbook Writer](https://agentscamp.com/skills/docs/runbook-writer.md): Write an operational runbook a half-asleep on-call engineer can execute at 3am — scoped to ONE alert, leading with how to confirm the problem, the copy-pasteable mitigation that stops user pain, then diagnosis, escalation, and verification. Use when an alert has no documented response, after an incident exposed a missing procedure, or when standing up on-call for a service. - [Source Concept Map Builder](https://agentscamp.com/skills/docs/source-concept-map-builder.md): Build a concept-node and relationship table from supplied document passages for one focal question, preserving source locations and contested edges before an AI mind map is drawn. - [Support Answer Card Builder](https://agentscamp.com/skills/docs/support-answer-card-builder.md): Draft one reusable customer-support answer card per supplied policy question, with approved source locators, applicability, required facts, concise answer wording, exclusions, and an escalation boundary. Use when turning documented policies into an answer library; do not invent policy or send replies. - [Branch Rebaser](https://agentscamp.com/skills/git/branch-rebaser.md): Rebase the current branch onto its base and walk every conflict methodically, resolving each by understanding both sides. Use when your feature branch has fallen behind main and you want a clean, linear history without clobbering changes. - [Commit Splitter](https://agentscamp.com/skills/git/commit-splitter.md): Split one big, mixed-up change into a series of small, atomic commits — each a single logical change that builds and passes tests on its own — by grouping hunks by intent and staging them piecemeal. Use when a working tree or a fat commit mixes a feature, a refactor, a bug fix, and formatting, or before opening a PR you want reviewers to actually read. - [Conventional Commits](https://agentscamp.com/skills/git/conventional-commits.md): Generate clear Conventional Commits messages from staged changes. Use when committing code and you want a well-structured, consistent commit message. - [Git Blame Investigator](https://agentscamp.com/skills/git/git-blame-investigator.md): Reconstruct why a line of code exists from Git history — find the originating commit, read its message and full diff for intent, and see through reformatting/rename commits with ignore-revs and the pickaxe — before you change or delete it. Use when a line looks wrong or pointless and you want to remove it, when tracing a regression to its commit, or when onboarding to unfamiliar code. - [PR Description](https://agentscamp.com/skills/git/pr-description.md): Draft a clear pull request description from the branch diff against its base. Use when you have a finished branch and want a reviewer-ready PR body before opening the PR. - [Asset Metadata Review Ledger](https://agentscamp.com/skills/marketing/asset-metadata-review-ledger.md): Map AI-suggested media catalog tags to a supplied controlled vocabulary and asset-version evidence, returning accept/reject/unresolved rows without inferring identities or usage rights. - [Brand Voice Profiler](https://agentscamp.com/skills/marketing/brand-voice-profiler.md): Derive a reusable brand voice guide from five to ten writing samples you paste in: tone axes scored with quoted evidence, use and avoid vocabulary, sentence rhythm, formatting habits, dos and don'ts, three before/after rewrites, and a rules block ready to paste into a SKILL.md, with every rule traced to a sample and disagreements between samples flagged instead of averaged. Use when your team writes in a recognizable voice that nobody has written down, or the guide you have was never derived from what you actually publish. - [Caption Correction Ledger](https://agentscamp.com/skills/marketing/caption-correction-ledger.md): Build a caption correction ledger from supplied caption text and verified listening notes, preserving cue IDs, original wording, proposed wording, timing, evidence and unresolved decisions. Use after automatic transcription and before a human approves edits; do not translate, clip or publish media. - [Content Repurposer](https://agentscamp.com/skills/marketing/content-repurposer.md): Turn one long-form piece you paste in (an article, transcript, webinar summary, or case study) into four channel-ready derivatives, an X thread, a LinkedIn post, a newsletter section, and a short-video script, each inside its channel's length and format limits, with every claim and number carried over exactly as written and any statistic the original does not source flagged instead of repeated as fact. Use when a finished piece has to go out on several channels and the derivatives must say only what the original says. - [Email Sequence Drafter](https://agentscamp.com/skills/marketing/email-sequence-drafter.md): Draft a four-to-six email sequence from a goal, an audience, and the product notes you paste in: a send-timing table, one job per email, three subject-line variants per email with character counts, a formatted and a plain-text version of each message, every product claim traced to your notes, and a compliance footer placeholder on every email. Use when a welcome, onboarding, launch, or nurture sequence has to be written from what the product actually does rather than from what a template assumes. - [Landing Page Copywriter](https://agentscamp.com/skills/marketing/landing-page-copywriter.md): Draft landing-page copy from an offer, an audience, and the proof points you actually have, as a complete section set (hero, problem, solution, how it works, proof, pricing framing, FAQ, and three CTA variants) in PAS or AIDA, with a [PROOF NEEDED] marker wherever a testimonial, number, or logo would normally sit but was not supplied. Use when a page needs first-draft copy and you would rather see the gaps than have them filled with invented social proof. - [Localization Glossary Builder](https://agentscamp.com/skills/marketing/localization-glossary-builder.md): Build a draft bilingual terminology glossary from supplied source-target samples and product references: retain term IDs, approved equivalents, protected strings, context, disallowed variants and unresolved translations. Use before reviewing localized marketing or help copy; do not translate a whole document or invent approved terms. - [Seo Content Brief Writer](https://agentscamp.com/skills/marketing/seo-content-brief-writer.md): Write an SEO content brief from a target keyword plus the SERP notes and competitor outlines you paste in: search intent with evidence, an angle the ranking pages under-serve, an H2/H3 outline, the entities and questions to cover, internal-link placeholders, a length-checked meta title and description, and a word-count target explained by the SERP rather than a rule of thumb. Use when a writer or a coding agent needs a brief before drafting an article and you do not want search volumes, difficulty scores, or rankings invented. - [Speech Cleanup Review Sheet](https://agentscamp.com/skills/marketing/speech-cleanup-review-sheet.md): Build a timed original-versus-processed speech cleanup review sheet from supplied listener observations, preserving variant IDs, intelligibility concerns, decisions and reviewer evidence. Use after processing a recording; do not transcribe, synthesize voices, edit audio or publish a master. - [Alerting Rules Tuner](https://agentscamp.com/skills/observability/alerting-rules-tuner.md): Cut alert noise and make every page mean something — rewrite alerting rules to fire on user-felt symptoms (error rate, latency SLO burn, failed requests) instead of causes (high CPU, full disk), with duration windows and severity routing so only urgent, actionable conditions reach a human. Use when on-call is fatigued by low-value pages, when real incidents get missed in the noise, or when alerts fire on causes rather than impact. - [Background Job Reliability Auditor](https://agentscamp.com/skills/observability/background-job-reliability-auditor.md): Audit scheduled jobs, queue consumers, workers, and asynchronous workflows for delivery assumptions, idempotency, retries, poison messages, concurrency, timeouts, checkpoints, shutdown, and observability. Use when a job duplicates work, silently stops, falls behind, fails only at scale, or needs review before production. - [Dashboard Designer](https://agentscamp.com/skills/observability/dashboard-designer.md): Design a service dashboard that answers one question at a glance — is the service healthy, and if not, where's the problem? — by structuring panels around RED/USE instead of dumping every metric. Use when a service has no dashboard, when the existing one is an unreadable metric wall, or during incident-readiness prep. - [Distributed Tracing Instrumenter](https://agentscamp.com/skills/observability/distributed-tracing-instrumenter.md): Instrument a service (or a chain of services) with OpenTelemetry so a single request can be followed end-to-end — context propagated across every hop including async/queue boundaries, spans at the boundaries that matter, deliberate trace-wide sampling, and trace_id stamped on log lines. Use when latency or failures span multiple services, when you have logs but can't reconstruct a request's full path, or when adopting OpenTelemetry. - [Incident Postmortem Writer](https://agentscamp.com/skills/observability/incident-postmortem-writer.md): Turn incident notes, alerts, chat logs, deploy history, and traces into a blameless, evidence-backed postmortem with impact, timeline, contributing conditions, detection and response gaps, and owned corrective actions. Use after a production incident, failed deployment, security event, or near miss when the team needs a durable learning document rather than a root-cause guess. - [SLO Definer](https://agentscamp.com/skills/observability/slo-definer.md): Turn a vague reliability goal into concrete SLIs, SLOs, an error budget, and burn-rate alerts — service-level indicators measured at the user-facing boundary, targets over a rolling window, and a written policy for what happens when the budget runs out. Use when a service has no defined reliability target, when on-call is noisy and alert-fatigued, or before you commit to an SLA you can't measure. - [Structured Logging Designer](https://agentscamp.com/skills/observability/structured-logging-designer.md): Design a structured (JSON) logging strategy with a stable field schema, correlation-ID propagation, and a disciplined level policy — then migrate ad-hoc string logs toward it. Use when logs are unsearchable plain text, when debugging a request across services means grepping multiple log streams by hand, or when standing up logging for a new service. - [Bundle Analyzer](https://agentscamp.com/skills/performance/bundle-analyzer.md): Analyze a JS/TS production bundle and surface the biggest size wins — heavy dependencies, duplicate packages, missing code-splitting, oversized polyfills, and dev/server code leaking into the client. Use when a bundle is too large and you need a ranked, actionable reduction plan. - [Cache Policy Designer](https://agentscamp.com/skills/performance/cache-policy-designer.md): Design a cache policy from data ownership, freshness, privacy, invalidation, and failure requirements across browser, CDN, reverse-proxy, application, and data caches. Use when adding caching, debugging stale responses, reviewing Cache-Control behavior, reducing origin load, or deciding whether a value can be cached safely at all. - [Cold Start Optimizer](https://agentscamp.com/skills/performance/cold-start-optimizer.md): Cut cold-start latency for serverless functions and slow-booting apps by measuring the init breakdown, then attacking the dominant phase — artifact size, eager imports, eager connections, or under-provisioned memory — instead of reflexively buying provisioned concurrency. Use when serverless p99 spikes on the first request, when a function times out during init, or when scale-to-zero is hurting user-facing latency. - [Flamegraph Analyzer](https://agentscamp.com/skills/performance/flamegraph-analyzer.md): Turn a CPU profile or flamegraph into a concrete optimization instead of guessing where the time goes: capture under a realistic workload with a sampling profiler, read the graph correctly (width = time, depth ≠ time), find the widest self-time leaves, ask if that work is necessary/redundant/algorithmically wrong, fix the biggest contributor, then re-profile. Use when code is CPU-bound and slow, a function is hot but you don't know which part, or you have a profile you can't interpret. - [Load Test Designer](https://agentscamp.com/skills/performance/load-test-designer.md): Design a defensible load test — a realistic workload model, a deliberate test type, and SLO-tied pass/fail thresholds — instead of a meaningless tight-loop script that hammers one endpoint. Use when validating capacity or SLOs before a launch or scaling event, when sizing infrastructure, or when an existing load test reports averages that nobody trusts. - [Memory Leak Hunter](https://agentscamp.com/skills/performance/memory-leak-hunter.md): Find and fix a memory leak in a running app: confirm it's a real leak under steady load, diff two heap snapshots to name the growing object and its retention path, cut the root reference that blocks collection, and re-run to confirm memory plateaus. Use when RSS climbs until OOM/restart, heap grows unbounded across a steady workload, or GC pauses worsen the longer the process runs. - [Prompt Cache Optimizer](https://agentscamp.com/skills/performance/prompt-cache-optimizer.md): Restructure an LLM call to maximize prompt-cache hit rate and add response/semantic caching — move the stable prefix (system prompt, instructions, few-shot, context) to the front and variable input to the end, set cache breakpoints, and measure the hit rate and savings. Use when repeated calls share large common context and token cost or latency is too high. - [React Render Profiler](https://agentscamp.com/skills/performance/react-render-profiler.md): Find and fix wasteful React re-renders by classifying the cause — unstable prop/callback/object identities, context value churn, state lifted too high, expensive work in render, or unvirtualized lists — confirming it with a measurement, then applying the one targeted fix and re-measuring. Use when a React UI is janky, slow to type in, or re-renders far more than the data actually changed. - [Web Vitals Optimizer](https://agentscamp.com/skills/performance/web-vitals-optimizer.md): Diagnose and fix Core Web Vitals — LCP, CLS, and INP — by treating real-user field data at p75 as the source of truth, using Lighthouse/WebPageTest only to find the at-fault element, script, or shift, then applying the one targeted fix per metric and re-measuring. Use when a page feels slow, scores poorly on PageSpeed/Lighthouse, or fails CWV in CrUX/RUM field data. - [Competitor Teardown](https://agentscamp.com/skills/product/competitor-teardown.md): Build a structured teardown of one competitor from the pages, screenshots, and pricing text you paste in: positioning, target customer, pricing model, a feature table with evidence, and the wedge opportunities where you could win, with every unknown flagged as something to research rather than guessed. Use when sizing up a competitor before a PRD, a pitch, or a pricing decision and you want an analysis grounded only in what they publish. - [Feedback Codebook Designer](https://agentscamp.com/skills/product/feedback-codebook-designer.md): Design a versioned coding codebook for recurring customer-feedback classification from a supplied pilot sample: define one code per job with inclusion rules, exclusions, verbatim examples, boundary cases, and an unresolved bucket. Use before labeling support tickets or survey comments, or when a feedback taxonomy has overlapping labels; do not synthesize interviews or rank product priorities. - [Investor Update Writer](https://agentscamp.com/skills/product/investor-update-writer.md): Draft a monthly investor update from the metrics and notes you paste: a three-line TL;DR, a metrics table with month-over-month deltas computed only from the numbers you supplied, wins, misses stated with their numbers, explicit asks, and a runway note. Never fills in a figure you did not give. Use when the update is due, the raw numbers are in a spreadsheet export or a scratch doc, and you want a draft that is honest about the misses. - [Job Scorecard Builder](https://agentscamp.com/skills/product/job-scorecard-builder.md): Draft a pre-interview job scorecard from supplied role tasks and human-selected priorities: map each criterion to task evidence, write observable three-level anchors and interview prompts, and preserve unresolved weighting choices. Use before interviewing; do not assess or rank candidates. - [Mvp Scope Cutter](https://agentscamp.com/skills/product/mvp-scope-cutter.md): Cut a feature list or PRD down to a ruthless version one: a keep / cut / later table with a one-line reason per row, plus the single smallest thing you could build to test the riskiest assumption. Use when first-release scope keeps growing, when an AI app builder session has produced more features than you can validate, or before you commit money or engineering time to a build. - [Prd Writer](https://agentscamp.com/skills/product/prd-writer.md): Turn a founder's raw idea, voice-memo transcript, or scattered bullet points into a product requirements document with a fixed section order: problem, users, jobs-to-be-done, scope, non-goals, success metrics, open questions, and risks. Use when an idea needs to be written down in a form a cofounder, contractor, or AI coding agent can build from, and it is not yet a document. - [Survey Path Test Builder](https://agentscamp.com/skills/product/survey-path-test-builder.md): Build a survey-paths.json test packet from approved single-choice question IDs, complete answer-to-destination rules and named endings; preserve missing routes for owner resolution. - [User Interview Synthesizer](https://agentscamp.com/skills/product/user-interview-synthesizer.md): Synthesize one or many customer interview transcripts or call notes into themes with participant counts, verbatim representative quotes, jobs-to-be-done statements, contradictions between what people said and did, and the questions to ask next. Never invents, merges, or rewords a quote. Use after a batch of discovery calls when the notes are piling up and you need to know what people actually said before deciding what to build. - [Circular Dependency Breaker](https://agentscamp.com/skills/refactor/circular-dependency-breaker.md): Detect and break a circular import — map the exact cycle with a real tool, then break the right edge by extracting the shared piece into a leaf module, inverting a layering dependency, merging two falsely-split modules, or (last resort) deferring an import. Use when you hit an import cycle error, an undefined-on-import or 'cannot access before initialization' bug, or a bundler/linter flags a cycle. - [Dead Code Finder](https://agentscamp.com/skills/refactor/dead-code-finder.md): Find genuinely unused code — unreferenced exports, unreachable files, and unused dependencies — and remove it safely with build/test verification. Use when trimming a codebase or untangling years of accreted cruft. - [Dependency Upgrade Planner](https://agentscamp.com/skills/refactor/dependency-upgrade-planner.md): Plan and de-risk a major dependency, framework, or runtime upgrade — map the full version path, read every intermediate migration guide, and pin the breaking changes to your actual call sites instead of bumping the number and hoping. Use when a key dependency is several majors behind, when a security advisory forces an upgrade, or before a framework migration. - [Extract Module](https://agentscamp.com/skills/refactor/extract-module.md): Split an overgrown file into cohesive, well-bounded modules — find the natural seams, design each new module's public interface before moving a line, then relocate one unit at a time keeping tests green. Use when a file has grown too large, mixes unrelated responsibilities, or every change to it forces unrelated diffs and merge conflicts. - [Feature Flag Retirer](https://agentscamp.com/skills/refactor/feature-flag-retirer.md): Retire stale feature flags by confirming each flag's decided final state, then collapsing every conditional to the winning branch and deleting the loser plus the now-dead code it reached. Use when temporary flags have outlived their rollout, when flag conditionals clutter the code, or during a flag-debt cleanup. - [Strangler Fig Migrator](https://agentscamp.com/skills/refactor/strangler-fig-migrator.md): Plan the incremental replacement of a legacy module or service using the strangler-fig pattern — grow new code around the old behind an interception seam until the old is dead, instead of a big-bang rewrite. Use when a legacy system is too risky to rewrite at once, or when migrating off a deprecated framework/dependency gradually while staying shippable and rollback-able at every step. - [Type Coverage Improver](https://agentscamp.com/skills/refactor/type-coverage-improver.md): Raise TypeScript type strictness incrementally — measure the any/implicit-any baseline, enable one strict sub-flag at a time, and fix the fallout per flag instead of all at once, keeping the typecheck green at every step. Use when a codebase is loosely typed, when you want strict mode on without a big-bang break, or when `any` keeps hiding bugs that surface in production. - [Canary Release Planner](https://agentscamp.com/skills/release/canary-release-planner.md): Design a canary / progressive rollout so a bad release reaches 1% of users instead of 100% — staged traffic with bake times, gating metrics compared against the concurrently-running stable baseline, and automated promote-or-rollback. Use when shipping a risky change, when you want automatic rollback on regression, or when moving off all-at-once deploys. - [Changelog From PRs](https://agentscamp.com/skills/release/changelog-from-prs.md): Draft a release changelog by summarizing merged pull requests since the last tag. Use when preparing a release or writing release notes. - [Release Notes Writer](https://agentscamp.com/skills/release/release-notes-writer.md): Write user-facing release notes — the curated 'what's new and what it means for you' — by starting from the real changes (git log / merged PRs / the changelog since the last release) and translating developer-speak into user impact, grouped by what the user cares about with breaking changes and required actions surfaced first. Use when shipping a release to users or customers and the raw commit log isn't something a user should read, when you need a published GitHub-release / blog / in-app announcement, or when a breaking change must be made unmissable so upgrades don't break. - [Release Readiness Reviewer](https://agentscamp.com/skills/release/release-readiness-reviewer.md): Review whether a specific release is ready to ship by checking scope, tests, compatibility, migrations, configuration, observability, rollout, rollback, support, security, and ownership against explicit evidence. Use before a production deployment, major version, risky feature launch, migration, or go/no-go meeting. - [Rollback Plan Writer](https://agentscamp.com/skills/release/rollback-plan-writer.md): Write a tested rollback and forward-recovery plan for a release, including triggers, commands, compatibility constraints, data handling, verification, ownership, and stop conditions. Use before high-risk deploys, schema or configuration changes, dependency upgrades, model or provider migrations, feature launches, and any release where 'just redeploy the old version' is incomplete or unsafe. - [SemVer Advisor](https://agentscamp.com/skills/release/semver-advisor.md): Decide the correct semantic-version bump — major, minor, or patch — by diffing a release range, mapping the changes onto the public API surface, and classifying each as breaking, additive, or a fix. Use before cutting a release when you are unsure whether changes are breaking, when a teammate proposes a bump you want to sanity-check, or when a behavior change has no signature change and you need to know if it is still breaking. - [Version Bumper](https://agentscamp.com/skills/release/version-bumper.md): Bump the project version everywhere it lives in one consistent pass — package.json, lockfile, nested/CLI package manifests, version constants, README badges, docs — then roll the changelog's Unreleased section under the new version and stage an annotated git tag. Use when you've already decided the new version (X.Y.Z or a pre-release like -rc.1) and need every artifact updated to the same value without drift, or before cutting a release. - [Cold Email Deliverability Auditor](https://agentscamp.com/skills/sales/cold-email-deliverability-auditor.md): Audit a cold email program's sending setup and message body against the things that decide whether mail reaches an inbox: SPF, DKIM and DMARC records with their alignment and policy, the DNS lookup limit, a separate sending domain versus the primary corporate one, domain age and warmup state, list hygiene with bounce and complaint exposure, spam-trigger language, link and image ratio, unsubscribe presence, and the volume ramp per mailbox. Findings come back by severity with the concrete fix and the order to apply them. Use when a sending domain is new, replies have collapsed, or nobody has checked the DNS records, the list, and the copy in the same sitting. - [Crm Export Auditor](https://agentscamp.com/skills/sales/crm-export-auditor.md): Audit a CRM CSV export for the data faults that make every report built on it wrong: exact and near-duplicate accounts and contacts, close dates already in the past on open deals, deals with no activity in the last N days, blank required fields by stage, single-threaded deals carrying one contact, records with no owner or a departed one, stage and amount combinations that contradict each other, and currency, date and country formats that drift between rows. Returns a fix list ordered by how much each fault distorts a reported number, with the record IDs to correct and which fixes are safe to apply in bulk. Use when a pipeline number is disputed, when a migration or a dedupe is being planned, or when nobody has checked the export the forecast is built on. - [Outreach Claim Checker](https://agentscamp.com/skills/sales/outreach-claim-checker.md): Check every factual and personalization claim in a drafted outreach email, or a batch of them, against the research the draft was written from: funding rounds, job titles and recent moves, tech stack, headcount, product launches and press, and named mutual connections. Each claim is marked sourced with the line that supports it, unsourced, contradicted, or stale, then unsupported specifics are cut or narrowed to what the research actually says, and every email leaves with a send, fix, or hold verdict. A source is never invented and never inferred. Use when outreach was personalized from AI or analyst research and nobody has verified the details before it reaches a prospect. - [RFP Requirement Mapper](https://agentscamp.com/skills/sales/rfp-requirement-mapper.md): Turn supplied RFP instructions and explicit addenda into a draft atomic compliance matrix: preserve requirement IDs and wording, split subparts visibly, map response/evidence locators, and flag missing mandatory or exception authority. Use before assembling a bid; do not write persuasive answers or invent compliance claims. - [Security Questionnaire Responder](https://agentscamp.com/skills/sales/security-questionnaire-responder.md): Draft answers to a prospect's security questionnaire — a SIG, a CAIQ, a VSA, or a custom spreadsheet — strictly from the documents the company supplies: the SOC 2 report, the DPA, the subprocessor list, a penetration test summary, architecture and access-control notes. Every answer is tagged sourced with the document and section it came from, needs-review, or not-applicable with the reason, and a control that appears in no document is left unanswered rather than asserted. Anything touching contractual terms, breach-notification windows, data residency, roadmap dates, or an exception is routed to Legal or Security by name. Use when a deal is blocked on a questionnaire and every answer has to survive an auditor reading it back. - [Auth Flow Reviewer](https://agentscamp.com/skills/security/auth-flow-reviewer.md): Read-only review of authentication AND authorization flows — session/token model, cookie flags, CSRF, token rotation, password-reset/email-verification, OAuth redirect/state, and per-route object-level access checks — for exploitable gaps. Use before shipping login/session/token code, when adding a protected route or sharing-by-URL feature, or during a security pass. Reports findings by severity with location, impact, and the concrete fix; never edits code. - [Data Retention Auditor](https://agentscamp.com/skills/security/data-retention-auditor.md): Audit where personal or sensitive data is collected, copied, retained, backed up, logged, exported, and deleted, then compare actual lifecycle behavior with stated policy. Use when adding a data field, preparing a privacy review, implementing account deletion, reducing stored data, or checking whether retention controls work across primary and secondary systems. - [Dependency Audit](https://agentscamp.com/skills/security/dependency-audit.md): Audit project dependencies for known vulnerabilities and turn the raw scanner output into a triaged, prioritized upgrade plan. Use when an audit is noisy, a CVE was reported, or you need to know which advisories actually matter. - [Least Privilege Auditor](https://agentscamp.com/skills/security/least-privilege-auditor.md): Audit application, CI, cloud, database, and agent permissions against observed usage, then produce a safe reduction plan with verification and rollback. Use when credentials have broad scopes, service roles grew organically, CI tokens can write too much, an agent or MCP server has excessive tools, or before a security review or compliance audit. - [License Compliance Checker](https://agentscamp.com/skills/security/license-compliance-checker.md): Audit the licenses of a project's dependencies for compatibility with how the project is distributed — flagging copyleft (GPL/AGPL/LGPL), missing or unknown licenses, and other obligations that conflict with your own license or SaaS/proprietary model. Use before shipping or open-sourcing, when adding a dependency, or when legal/procurement asks for a license inventory. This is a licensing review, not a vulnerability scan. - [LLM Guardrails Designer](https://agentscamp.com/skills/security/llm-guardrails-designer.md): Design input and output guardrails for an LLM app — decide what to check (injection patterns, PII, secrets, policy, schema, leakage, toxicity), place them as input vs. output rails, implement with a library like NeMo Guardrails or LLM Guard, and fail closed. Use when adding a safety/validation layer around an LLM, not relying on the prompt alone. - [Prompt Pii Redactor](https://agentscamp.com/skills/security/prompt-pii-redactor.md): Detect and redact PII and secrets from prompts (and logs/traces) before they reach an LLM provider — mask or tokenize emails, phone numbers, names, IDs, and API keys, reversibly where the response needs the real values back. Use when sending user or document data to a third-party model, or when LLM request logs may capture sensitive data. - [RBAC Designer](https://agentscamp.com/skills/security/rbac-designer.md): Design the authorization model itself — fine-grained permissions on resources composed into roles, with the right amount of resource/tenant scoping — instead of scattering role-name checks through handlers. Use when building multi-user or multi-tenant authorization, when `if user.isAdmin` checks are sprawling across the codebase, or when 'who can do what' needs a real model rather than ad-hoc gates. - [Secret Scanner](https://agentscamp.com/skills/security/secret-scanner.md): Scan a repo or a diff for committed secrets — API keys, tokens, private keys, .env files, and high-entropy strings — then triage real leaks from fixtures. Use before pushing, in review, or when a credential may have leaked. - [Security Headers Hardener](https://agentscamp.com/skills/security/security-headers-hardener.md): Audit and harden a web app's or API's HTTP security headers — Content-Security-Policy, HSTS, X-Content-Type-Options, frame-ancestors, Referrer-Policy, Permissions-Policy, and CORS — and produce a staged rollout that won't break the site. Use before a launch, during a security pass, or when a scanner (Mozilla Observatory, securityheaders.com, a pentest) flags missing or weak headers. Audits and edits header config; rolls CSP out Report-Only first. - [Threat Model Builder](https://agentscamp.com/skills/security/threat-model-builder.md): Build a practical threat model for a feature or system using STRIDE — diagram the data flow, mark trust boundaries, enumerate concrete threats where data crosses them, and prioritize by likelihood × impact so security is reasoned about before shipping instead of bolted on after. Use when designing a feature that touches auth, money, or sensitive data, running a security design review, or hardening before a launch. - [Accessibility Regression Auditor](https://agentscamp.com/skills/testing/accessibility-regression-auditor.md): Audit a UI change for accessibility regressions by combining automated checks with keyboard, focus, semantic, name-role-value, contrast, zoom, and screen-reader-oriented inspection. Use when reviewing a component or pull request, adding a dialog or form, changing navigation, or investigating an accessibility failure that a linter alone cannot explain. - [Contract Test Designer](https://agentscamp.com/skills/testing/contract-test-designer.md): Design consumer-driven contract tests between services so an API provider can't break its consumers unnoticed — without slow, flaky full end-to-end environments. Use when independent services or teams integrate over an API, when integration bugs only surface in staging or prod, or when E2E suites are too slow and brittle to catch breaking API changes. - [Coverage Gap Finder](https://agentscamp.com/skills/testing/coverage-gap-finder.md): Run the project's coverage tool and identify the highest-value untested paths — error branches, edge cases, and critical modules — then propose specific test cases for each gap. Use when you have a coverage report but don't know where new tests will pay off most. - [Flaky Test Diagnoser](https://agentscamp.com/skills/testing/flaky-test-diagnoser.md): Diagnose a test that passes and fails without relevant code changes by reproducing the instability, classifying its trigger, and isolating the shared state, timing, ordering, concurrency, randomness, or environment dependency behind it. Use when CI retries hide failures, a test fails only in the suite, or a failure cannot be reproduced reliably on one machine. - [Integration Test Designer](https://agentscamp.com/skills/testing/integration-test-designer.md): Design integration tests that exercise components against REAL collaborators — actual database, queue, HTTP boundary — at a deliberately chosen seam, instead of a unit suite that mocks everything or a slow flaky full E2E. Use when bugs slip past green unit tests, when wiring or contracts between layers break in production, or when a mocked DB test passes but the real query/migration/serialization fails. - [Mock Data Factory](https://agentscamp.com/skills/testing/mock-data-factory.md): Generate a typed mock/fixture factory for a given type, interface, or schema, inferring believable values from field names and types. Use when tests or local dev need realistic, type-safe sample data with per-field overrides. - [Mutation Test Runner](https://agentscamp.com/skills/testing/mutation-test-runner.md): Measure whether a test suite actually catches bugs by running mutation testing — introduce small faults into the code and check which ones a test kills versus which slip through silently. Use when line coverage is high but bugs still ship, when you suspect tests assert weakly, or to find the exact assertions a suite is missing. - [Property Test Designer](https://agentscamp.com/skills/testing/property-test-designer.md): Design property-based tests — generate hundreds of random inputs and assert invariants that must hold for ALL of them — to surface the edge cases hand-picked examples never reach. Use when code has a large input space (parsers, serializers, encoders, math, data transforms), when a bug keeps slipping through despite green example tests, or when you can't enumerate every case worth checking. - [Regression Test Writer](https://agentscamp.com/skills/testing/regression-test-writer.md): Turn a reported bug into the smallest test that fails for the real reason before the fix and passes afterward. Use when reproducing a defect, reviewing a bug fix with no guard test, converting an incident into permanent coverage, or preventing a previously fixed edge case from returning. - [Test Scaffolder](https://agentscamp.com/skills/testing/test-scaffolder.md): Scaffold a test file with sensible cases for a given module or function. Use when adding tests to untested code and you want a fast, structured starting point. - [Agent Memory Designer](https://agentscamp.com/skills/workflow/agent-memory-designer.md): Design a project's CLAUDE.md and memory hierarchy by exploring the repo to learn its real build/test/lint commands, architecture, and non-obvious gotchas, then writing a concise, skimmable memory that keeps only what belongs in context. Use when onboarding a repo to Claude Code with no CLAUDE.md, or when an existing one is bloated, stale, or being ignored. - [Claude Settings Auditor](https://agentscamp.com/skills/workflow/claude-settings-auditor.md): Audit every Claude Code settings layer — user, project, local, and managed — and report the effective merged configuration with its risks: over-broad Bash allows, missing deny rules for secrets, bypassPermissions defaults, unvetted MCP servers and hooks, and rules that never match. Use before trusting a new repo's checked-in settings, or to harden your own before handing the agent more autonomy. - [Dev Container Designer](https://agentscamp.com/skills/workflow/devcontainer-designer.md): Design a reproducible dev environment (Dev Container / Docker) so onboarding is one command and 'works on my machine' dies — by detecting the project's real stack and versions, authoring a devcontainer.json (+ Dockerfile/compose) that pins the runtime to what the repo targets, wires dependent services, caches dependencies, and injects secrets instead of baking them. Use when new contributors struggle to set up the project, when environment drift causes inconsistent behavior, or when standardizing tooling across a team. - [Dockerfile Optimizer](https://agentscamp.com/skills/workflow/dockerfile-optimizer.md): Shrink and harden an existing Dockerfile — multi-stage builds, cache-friendly layer order, a lean pinned base image, a .dockerignore, and a non-root runtime user — without changing what the image runs. Use when an image is too large, builds are slow because the cache never hits, or a scan flags the container running as root. - [GitHub Actions Optimizer](https://agentscamp.com/skills/workflow/github-actions-optimizer.md): Make a GitHub Actions workflow faster, cheaper, and harder to attack — by profiling where wall-clock and billed minutes actually go, then adding content-keyed caching, matrix/job parallelism, run-cancellation, and path filters, and hardening the supply chain (SHA-pinned actions, least-privilege GITHUB_TOKEN, safe fork-PR handling). Use when CI is slow or queues, when a repo burns Actions minutes, or before trusting a workflow that runs on untrusted pull requests. - [Hook Writer](https://agentscamp.com/skills/workflow/hook-writer.md): Turn a plain-language automation request — 'format every file Claude edits', 'block writes to migrations', 'notify me when input is needed' — into a working Claude Code hook: the right event, a safe tested script, and the settings.json registration at the right scope. Use when you want a hook but don't want to hand-write the matcher, stdin JSON parsing, and exit-code plumbing. - [Human In The Loop Gate](https://agentscamp.com/skills/workflow/human-in-the-loop-gate.md): Add a human approval checkpoint to an agent so it pauses before a risky or irreversible action (spending money, deleting data, sending messages, merging code) and resumes only after a human approves. Use when an agent acts autonomously on consequential operations. - [Localization Readiness Auditor](https://agentscamp.com/skills/workflow/localization-readiness-auditor.md): Audit an application or feature for localization readiness across extracted messages, interpolation, pluralization, date and number formatting, layout expansion, right-to-left behavior, sorting, search, assets, metadata, and test coverage. Use before adding a locale, reviewing hard-coded UI text, or investigating a feature that breaks outside English. - [Monorepo Boundary Auditor](https://agentscamp.com/skills/workflow/monorepo-boundary-auditor.md): Audit a monorepo's package boundaries, dependency direction, public entry points, ownership, build graph, and affected-test rules, then report cycles and cross-layer imports with a staged repair plan. Use when packages are tightly coupled, builds invalidate too much, teams import internals, dependency cycles appear, or a monorepo migration needs enforceable architecture boundaries. - [Plugin Scaffolder](https://agentscamp.com/skills/workflow/plugin-scaffolder.md): Scaffold a complete, valid Claude Code plugin from a description — the .claude-plugin/plugin.json manifest, component directories (skills, agents, commands, hooks, MCP config), portable ${CLAUDE_PLUGIN_ROOT} wiring, a local test loop with --plugin-dir, and a marketplace.json for distribution. Use when turning scattered .claude/ customizations into one installable, versioned package. - [Prompt Optimizer](https://agentscamp.com/skills/workflow/prompt-optimizer.md): Diagnose why a prompt underperforms and rewrite it with the technique that fixes it — clearer structure, few-shot examples, an explicit output contract, or reasoning scaffolding — returning an optimized prompt, the rationale for every change, and what to measure to confirm the lift. Use when a prompt is flaky, verbose, drifting in format, or just not good enough. - [Skill Auditor](https://agentscamp.com/skills/workflow/skill-auditor.md): Audit an installed set of Claude Code skills for the failure modes that make them misfire — overlapping descriptions that misroute tasks, trigger phrasing that never matches, bloated bodies, missing boundaries, and over-broad tool grants — and return a prioritized fix list with rewritten descriptions. Use when a skill fires on the wrong tasks, never fires at all, or a skills folder has grown past what anyone reviews. - [Study Card Builder](https://agentscamp.com/skills/workflow/study-card-builder.md): Draft source-grounded study flashcards from approved notes for one stated learning goal, with question, answer, exact evidence quote, source locator and unresolved status. Use before learner review; do not generate classroom assessments, final grades or a study schedule. ## Commands Reusable slash commands that automate repeatable workflows in Claude Code. - [Count Feedback](https://agentscamp.com/commands/analytics/count-feedback.md): Count a single-label feedback CSV with code: validate record IDs, collapse exact duplicate rows, reject conflicting rows for one ID, and report records, distinct known respondents, missing identities, and label totals without inventing prevalence. - [Define Metric](https://agentscamp.com/commands/analytics/define-metric.md): Write or refine a metric definition — name, plain-language meaning, grain, filters, source tables and columns, edge cases, and owner — into analysis/metrics/.md, after searching the repo for a definition that already exists. - [First Look](https://agentscamp.com/commands/analytics/first-look.md): Read the first rows and the true row count of a CSV or spreadsheet, profile it against a fixed checklist, and write the result to analysis/profiles/.md so the dataset's condition is on record before anyone queries it. - [Audit Accessibility](https://agentscamp.com/commands/analyze/audit-accessibility.md): Audit a component or page for accessibility against WCAG — semantics, names, keyboard, ARIA, contrast, forms, motion. - [Explain Error](https://agentscamp.com/commands/analyze/explain-error.md): Diagnose an error message or stack trace and propose a fix. - [Trace Data Flow](https://agentscamp.com/commands/analyze/trace-data-flow.md): Trace how a value, field, or variable flows through the codebase from source to sink. - [DB Migrate](https://agentscamp.com/commands/db/db-migrate.md): Generate and apply a database migration the safe way — using the project's migration tool, with expand-contract discipline for breaking changes, lock-free DDL, and a reversible up/down. - [Scaffold a pgvector Schema & HNSW Index](https://agentscamp.com/commands/db/scaffold-pgvector-schema.md): Scaffold a production-ready pgvector schema and HNSW index for a corpus — matching the project's migration tooling, distance metric, and embedding dimensions. - [Seed Data](https://agentscamp.com/commands/db/seed-data.md): Generate realistic, referentially-consistent seed data and a re-runnable seed script from your actual schema — types and constraints respected, plausible values, FK-dependency insert order, idempotent, never aimed at production. - [Critique Screen](https://agentscamp.com/commands/design/critique-screen.md): Read a UI screenshot from a path or URL, run the fixed design critique checklist over it, and write the report to design/critiques/ without touching the design or the code. - [Design Tokens](https://agentscamp.com/commands/design/design-tokens.md): Extract design tokens from a stylesheet, a Tailwind or theme config, or a screenshot, then create or update design/tokens.json and report every added, changed, and removed token. - [Add Docstrings](https://agentscamp.com/commands/docs/add-docstrings.md): Add or improve docstrings for the public API of a file or symbol. - [Check Obligation Dates](https://agentscamp.com/commands/docs/check-obligation-dates.md): Check a human-reviewed obligation CSV against an explicit as-of date with Python: validate calendar dates and status fields, list overdue or due-today open rows, missing owners/dates, and recorded late/future completions. Never infer legal deadlines, breach, or contract status from clauses. - [Check Procedure Graph](https://agentscamp.com/commands/docs/check-procedure-graph.md): Check explicitly recorded SOP step dependencies with Python: validate IDs and prerequisite references, compute deterministic execution layers for acyclic graphs, and report steps blocked by a cycle without executing the procedure. - [Explain Code](https://agentscamp.com/commands/docs/explain-code.md): Explain what the given code does, in clear prose with a short summary. - [Snapshot Handoff](https://agentscamp.com/commands/docs/snapshot-handoff.md): Create a deterministic inventory of an approved handoff directory: record relative file paths, byte lengths, and SHA-256 hashes with code, list skipped symlinks and unreadable files, and write a snapshot without interpreting project status or changing source files. - [Update README](https://agentscamp.com/commands/docs/update-readme.md): Update the README to reflect the current scripts, structure, and features of the repo. - [Clean Branches](https://agentscamp.com/commands/git/clean-branches.md): Safely prune merged and stale Git branches: drop dead remote-tracking refs, list merged candidates for review, then delete with the safe -d variant. - [Commit](https://agentscamp.com/commands/git/commit.md): Stage changes and write a Conventional Commits message describing them. - [Create PR](https://agentscamp.com/commands/git/create-pr.md): Push the current branch and open a GitHub pull request with a generated title and body. - [Git Bisect](https://agentscamp.com/commands/git/git-bisect.md): Drive git bisect to find the exact commit that introduced a regression. - [Git Undo](https://agentscamp.com/commands/git/git-undo.md): Safely reverse the last Git operation in the current repo — pick the right tool (restore, reset --soft/--mixed, revert, or reflog recovery) based on what happened and whether it was already pushed, and confirm before anything destructive. - [Resolve Merge Conflicts](https://agentscamp.com/commands/git/resolve-conflict.md): Walk through resolving the in-progress merge, rebase, or cherry-pick conflict in the current repo by understanding both sides, then verify before continuing. - [Sync Branch](https://agentscamp.com/commands/git/sync-branch.md): Fetch and rebase the current branch onto its base, resolving conflicts and verifying the build. - [Brand Check](https://agentscamp.com/commands/marketing/brand-check.md): Score a draft file against the project's brand voice guide (brand-voice.md or a brand-voice skill) and list every violation with a rewrite, without editing it. - [Check Translation Tokens](https://agentscamp.com/commands/marketing/check-translation-tokens.md): Compare recognized named placeholders in a source-target translation CSV with Python, preserving token multiplicity and flagging blank targets. Supports exact {name}, {{name}}, and %(name)s/%(name)d forms; does not validate full ICU, HTML, positional placeholders, or translation meaning. - [Repurpose](https://agentscamp.com/commands/marketing/repurpose.md): Run content-repurposer on a file or pasted text and write the X thread, LinkedIn post, newsletter section, and video script to content/repurposed/. - [Add Caching](https://agentscamp.com/commands/perf/add-caching.md): Add a caching layer to one expensive function or endpoint correctly — confirm it's cacheable, design the cache key/TTL/layer/invalidation, handle stampedes, wrap the call in one place, and report the design. - [Find N+1 Queries](https://agentscamp.com/commands/perf/find-n-plus-one.md): Scan code read-only for N+1 query patterns — loops that query per iteration and handlers that fan out per-row — and report each with a location, why it is N+1, and the concrete eager-load/batch/set-based fix. - [Profile Postgres Queries](https://agentscamp.com/commands/perf/profile-postgres-queries.md): Profile a Postgres workload to find the queries actually costing you — rank by total time with pg_stat_statements, EXPLAIN the worst offenders, and recommend the highest-leverage fix. - [Set Perf Budget](https://agentscamp.com/commands/perf/set-perf-budget.md): Define and enforce a cost and latency budget for an LLM feature or endpoint — set p95/p99 latency and cost-per-request ceilings, instrument to measure them against real traffic, and wire a check that fails when the budget is breached. - [Breakdown Task](https://agentscamp.com/commands/plan/breakdown-task.md): Decompose a task into an ordered checklist of small, verifiable steps. - [Estimate Effort](https://agentscamp.com/commands/plan/estimate-effort.md): Produce a grounded effort and complexity estimate for a task by exploring the codebase read-only. - [Plan Feature](https://agentscamp.com/commands/plan/plan-feature.md): Explore the codebase and produce an implementation plan for a feature. - [Write Design Doc](https://agentscamp.com/commands/plan/write-design-doc.md): Explore the codebase and write a decision-oriented design doc / RFC for a feature or system change. - [Check Scorecard](https://agentscamp.com/commands/product/check-scorecard.md): Validate a draft job-interview scorecard JSON with Python: unique criteria, exactly three nonblank behavioral anchor slots, task-source and question references, and positive human-supplied decimal weights totaling exactly one. Never read or calculate candidate scores. - [PRD](https://agentscamp.com/commands/product/prd.md): Turn an idea, pasted notes, or a notes file into a structured PRD and write it to docs/prd.md, or print it when there is no project. - [Scope MVP](https://agentscamp.com/commands/product/scope-mvp.md): Cut a PRD or feature list to a version one: a keep / cut / later table with reasons plus the smallest build that tests the riskiest assumption, written to docs/mvp-scope.md. - [Extract Function](https://agentscamp.com/commands/refactor/extract-function.md): Extract a code region into a well-named function and update the call site. - [Optimize Imports](https://agentscamp.com/commands/refactor/optimize-imports.md): Remove unused imports and organize the rest in a file or directory per the project's conventions — preferring the project's own import tool where one is configured — without changing behavior. - [Refactor](https://agentscamp.com/commands/refactor/refactor.md): Refactor the target for readability and structure without changing behavior. - [Rename Symbol](https://agentscamp.com/commands/refactor/rename-symbol.md): Safely rename a symbol project-wide, distinguishing the real symbol from coincidental substring matches. - [Benchmark Rerankers](https://agentscamp.com/commands/review/benchmark-rerankers.md): Measure whether adding a reranker actually improves retrieval, by scoring reranked vs. un-reranked results on a labeled query set. - [Check Annotation Batch](https://agentscamp.com/commands/review/check-annotation-batch.md): Check annotation-batch.json for approved labels, distinct response reviewers, exact text evidence and unresolved disagreements while preserving the input. - [Check Audio Review Sheet](https://agentscamp.com/commands/review/check-audio-review-sheet.md): Check audio-review-sheet.json for bounded review intervals, explicit original/processed observations and listener confirmation before accepted cleanup decisions, printing findings only. - [Check Caption Timing](https://agentscamp.com/commands/review/check-caption-timing.md): Check captions.srt in the current directory for supported SRT syntax, sequential cue numbers, increasing timestamps and overlapping cues, printing results without changing captions. - [Check Entity Match Ledger](https://agentscamp.com/commands/review/check-entity-match-ledger.md): Check entity-match-ledger.json for source-linked evidence values, duplicate pairs, reviewer presence and contradictory match groups without merging records. - [Check Evidence Table](https://agentscamp.com/commands/review/check-evidence-table.md): Check a study evidence CSV against a source registry with code: validate required columns and cells, row IDs, source references, duplicate study/outcome/time-point keys, and allowed status values, then report structural errors without judging research claims. - [Check Exit Ticket Map](https://agentscamp.com/commands/review/check-exit-ticket-map.md): Check a local exit-ticket-map.json for objective/question IDs, objective coverage, supported answer evidence and unresolved review status, without editing it. - [Check OCR Correction Ledger](https://agentscamp.com/commands/review/check-ocr-correction-ledger.md): Check ocr-correction-ledger.json for page/span references, exact original text slices, overlapping proposed edits and reviewer evidence on verified corrections, without applying changes. - [Check Storyboard Coverage](https://agentscamp.com/commands/review/check-storyboard-coverage.md): Check storyboard-coverage.json for unique beat/shot IDs, cited beat excerpts, positive shot durations and uncovered script beats, without changing the script or storyboard. - [Check Study Card Map](https://agentscamp.com/commands/review/check-study-card-map.md): Check a local study-card-map.json for valid card/source IDs, exact evidence quotes, duplicate questions and unresolved-card status, without editing it. - [Check Survey Paths](https://agentscamp.com/commands/review/check-survey-paths.md): Check survey-paths.json for complete single-choice routes, cycles, reachable nodes and expected test paths without editing the file or contacting a form service. - [Find Bug](https://agentscamp.com/commands/review/find-bug.md): Investigate a reported symptom, form hypotheses, and locate the root cause. - [Red Team LLM](https://agentscamp.com/commands/review/red-team-llm.md): Red-team an LLM app or agent for prompt injection, jailbreaks, and data leakage — probe the real attack surface (input, RAG, tools, system prompt) with adversarial inputs and report what got through and how to fix it. - [Review PR](https://agentscamp.com/commands/review/review-pr.md): Review a pull request for correctness, security, and style, and summarize findings. - [Review Tests](https://agentscamp.com/commands/review/review-tests.md): Review the quality of a test suite, not just whether it passes — find weak assertions, missing edge cases, and tests coupled to implementation. - [Security Scan](https://agentscamp.com/commands/review/security-scan.md): Scan the current diff or given paths for security vulnerabilities. - [Audit CRM](https://agentscamp.com/commands/sales/audit-crm.md): Audit a CRM CSV export for duplicates, past close dates, stale deals, missing required fields, single-threaded deals, owner gaps and format drift, and write a ranked fix list with the record IDs. - [Check Outreach](https://agentscamp.com/commands/sales/check-outreach.md): Fact-check a drafted outreach email, or a folder of them, against the research files it was written from and write an annotated report with a claims table, without editing the draft. - [Check RFP Coverage](https://agentscamp.com/commands/sales/check-rfp-coverage.md): Check human-reviewed RFP requirement and response-matrix CSVs with Python: validate unique IDs and explicit flags, count supplied statuses including absent rows, and list mandatory attention items or ready rows missing required references. Does not prove claim accuracy or submit a bid. - [Add Human Approval Step](https://agentscamp.com/commands/scaffold/add-human-approval.md): Scaffold a human-in-the-loop approval gate into an agent so it pauses before a consequential action and resumes after approval. - [Add a Streaming LLM Endpoint](https://agentscamp.com/commands/scaffold/add-streaming-endpoint.md): Scaffold a token-streaming LLM endpoint — server-side streaming plus the client handler — so responses render incrementally instead of after a long wait. - [New Component](https://agentscamp.com/commands/scaffold/new-component.md): Scaffold a new UI component matching the project conventions. - [Scaffold CLI Command](https://agentscamp.com/commands/scaffold/scaffold-cli.md): Scaffold a new subcommand for an existing CLI (or a minimal new CLI) in the project's language — flag/argument parsing, --help text, input validation, and correct exit codes — matching the framework and conventions already in use. - [Scaffold Dockerfile](https://agentscamp.com/commands/scaffold/scaffold-dockerfile.md): Scaffold a production-grade multi-stage Dockerfile and .dockerignore for the current project. - [Scaffold GitHub Action](https://agentscamp.com/commands/scaffold/scaffold-github-action.md): Scaffold a hardened GitHub Actions workflow for a stated goal, wired to the project's real test/lint/build commands. - [Scaffold RAG Pipeline](https://agentscamp.com/commands/scaffold/scaffold-rag-pipeline.md): Scaffold a Retrieval-Augmented Generation pipeline — ingestion (load, chunk, embed, upsert) and retrieval (search, rerank, grounded prompt with citations) — fitted to the project's stack. - [Scaffold a vLLM Serving Config](https://agentscamp.com/commands/scaffold/scaffold-vllm-config.md): Scaffold a vLLM serving config for a model on a target GPU — pick precision/quantization and parallelism to fit, set batching and context length, and expose an OpenAI-compatible server. - [Check Support Cases](https://agentscamp.com/commands/testing/check-support-cases.md): Validate a local JSONL support-answer testset with Python: unique case IDs, exact fields, answer/clarify/escalate expectations, required answer-card references and sources, and explicit escalation reasons; report counts without running a support bot. - [Fix Failing Test](https://agentscamp.com/commands/testing/fix-failing-test.md): Diagnose and fix a failing test by finding the real root cause. - [Hunt Flaky Tests](https://agentscamp.com/commands/testing/flaky-test-hunt.md): Reproduce a flaky test, find the real source of nondeterminism, and fix the cause. - [Generate E2E Test](https://agentscamp.com/commands/testing/generate-e2e-test.md): Scaffold a resilient end-to-end test for a user flow grounded in the real UI. - [Run Evals](https://agentscamp.com/commands/testing/run-evals.md): Run the project's LLM evaluation suite (DeepEval, promptfoo, or RAGAS) and report scores against thresholds before a merge. - [Write Tests](https://agentscamp.com/commands/testing/write-tests.md): Generate tests covering the happy path and edge cases for the given target. - [Add MCP Server](https://agentscamp.com/commands/workflow/add-mcp-server.md): Add an MCP server to the current project the safe way — pick the transport and scope, wire secrets through env vars, vet provenance, and verify the connection before trusting it. - [Create Skill](https://agentscamp.com/commands/workflow/create-skill.md): Scaffold a new Claude Code skill into .claude/skills//SKILL.md — a model-invoked capability with a trigger-rich description, scoped tools, and a lean body that pushes detail into resource files. - [Create Slash Command](https://agentscamp.com/commands/workflow/create-slash-command.md): Scaffold a new Claude Code slash command into .claude/commands/ — a valid Markdown file with frontmatter, a least-privilege allowed-tools allowlist, and a $ARGUMENTS-driven body of numbered steps ending in a Report. - [Create Subagent](https://agentscamp.com/commands/workflow/create-subagent.md): Scaffold a new Claude Code subagent definition file into .claude/agents/ with a routing-ready description, scoped tools, and a system prompt. - [Setup Claude CI](https://agentscamp.com/commands/workflow/setup-claude-ci.md): Wire Claude Code into this repo's CI the safe way — install the GitHub App or scaffold the workflow YAML, scope permissions to the minimum, set secrets correctly, and verify with a real trigger. - [Setup Pre-commit Hooks](https://agentscamp.com/commands/workflow/setup-precommit-hooks.md): Set up fast pre-commit hooks that catch problems before they land — detect the repo's existing stack and hook mechanism, run lint/format/typecheck plus a secret scan on staged files only, keep the slow test suite in CI, and make the setup reproducible for the whole team. ## By role Curated reading orders per reader role, each followed by the tools, skills, and agents picked for that work. - [Developers](https://agentscamp.com/for/developers): Claude Code, coding agents, skills, MCP, and the engineering guides, tools, and installables that make AI-assisted development actually ship. - [AI engineers](https://agentscamp.com/for/ai-engineers): Building with LLMs, not just coding with them: RAG, evals, agents, inference, and the SDKs, skills, and subagents that get AI features to production. - [DevOps & platform engineers](https://agentscamp.com/for/devops): Claude Code in CI, sandboxed agents, SLOs and incident response — the infrastructure agents, skills, and commands that keep AI-assisted delivery boring. - [Security & AI safety](https://agentscamp.com/for/security): Prompt injection, the agentic OWASP top 10, red-teaming, and the auditing skills and agents that make AI-assisted and AI-powered work safe to ship. - [Sales & revenue teams](https://agentscamp.com/for/sales): Claude for sales work: the official sales plugin, prospect research you can actually source, CRM and deliverability audits, and which tools connect to Claude. - [Founders & non-technical builders](https://agentscamp.com/for/founders): Build and run a product without an engineering team: Claude Code in plain language, AI app builders, automation, and which Claude plan to pay for. - [Marketers & content teams](https://agentscamp.com/for/marketers): Claude for marketing and content work: brand-voice skills, research workflows, the official marketing plugin, and the AI writing tools worth paying for. - [Designers](https://agentscamp.com/for/designers): Claude Design, Figma-to-code with Claude Code, design-system upkeep, and the AI design and image tools that fit a working designer's stack. - [Data & analytics teams](https://agentscamp.com/for/analysts): Claude for data analysis: Claude for Excel, text-to-SQL, notebooks with Claude Code, checking AI analyses, and the analytics tools that hold up. ## Network - [OptimizeCamp](https://optimizecamp.com): Audit & optimize content for AI search - [GritShip](https://gritship.com): Lightweight project management for makers - [SurePrompts](https://sureprompts.com): Free AI prompt generator & builder ## Optional - [Full content](https://agentscamp.com/llms-full.txt): every page concatenated as Markdown - [How to use](https://agentscamp.com/how-to-use): installing agents, skills, and commands - [RSS feed](https://agentscamp.com/feed.xml): newest additions