# AgentsCamp

> A curated hub for everything AI — agents, skills, guides, tools, and commands for building with AI coding agents.

Everything here is copy-paste/installable and format-validated for use with AI coding agents (Claude Code). Each page below has a clean Markdown twin at the same URL with a `.md` suffix.

## Agents

Drop-in Claude Code subagents with focused system prompts — code review, debugging, architecture, and more.

- [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.
- [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".
- [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".
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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".
- [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.
- [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".
- [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.

## Skills

Packaged SKILL.md capabilities that extend Claude with on-demand expertise.

- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.

## 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 decomposing big tasks and coordinating multiple agents.
- [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.
- [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.
- [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.
- [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.
- [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 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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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 for an agent or task.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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 — Cline, Aider, Codex CLI, Roo Code, and more. Who should bring their own model, and when to stay in the terminal.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.

## Tools

A curated directory of AI coding tools, editors, agents, and MCP servers.

- [AgentOps](https://agentscamp.com/tools/agentops.md): Observability for AI agents — session replay, cost and latency tracking, and debugging for multi-step runs.
- [Aider](https://agentscamp.com/tools/aider.md): AI pair programming in your terminal, with strong Git integration.
- [Amp](https://agentscamp.com/tools/amp.md): Sourcegraph's agentic coding tool — a CLI and editor extensions tuned for frontier-model coding.
- [Arize Phoenix](https://agentscamp.com/tools/arize-phoenix.md): An open-source LLM observability and evaluation tool built on OpenTelemetry, runnable anywhere.
- [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.
- [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.
- [Braintrust](https://agentscamp.com/tools/braintrust.md): An end-to-end platform for evaluating, iterating on, and observing LLM apps, with a prompt playground.
- [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.
- [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.
- [Cline](https://agentscamp.com/tools/cline.md): An open-source autonomous coding agent for VS Code.
- [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.
- [Continue](https://agentscamp.com/tools/continue.md): An open-source IDE extension for building custom AI coding assistants.
- [CrewAI](https://agentscamp.com/tools/crewai.md): A Python framework for orchestrating role-playing AI agents as collaborating 'crews', plus event-driven flows.
- [Cursor](https://agentscamp.com/tools/cursor.md): An AI-first code editor built on VS Code with deep in-editor agent features.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [Github Copilot](https://agentscamp.com/tools/github-copilot.md): GitHub’s AI pair programmer with inline completions and an agent mode.
- [Goose](https://agentscamp.com/tools/goose.md): Block's open-source, on-machine AI agent that is MCP-native and model-agnostic, with a CLI and desktop app.
- [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.
- [Instructor](https://agentscamp.com/tools/instructor.md): Get structured, validated output from LLMs using plain type definitions, with automatic retries on validation failure.
- [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.
- [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.
- [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.
- [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.
- [Lovable](https://agentscamp.com/tools/lovable.md): An AI app builder that turns natural-language prompts into shippable full-stack web apps.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [promptfoo](https://agentscamp.com/tools/promptfoo.md): An open-source CLI for testing, comparing, and red-teaming LLM prompts, models, and apps.
- [Qdrant](https://agentscamp.com/tools/qdrant.md): An open-source vector database written in Rust, built for low-latency similarity search at scale.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [Zed](https://agentscamp.com/tools/zed.md): A high-performance, multiplayer code editor with built-in AI assistance.

## Commands

Reusable slash commands that automate repeatable workflows in Claude Code.

- [Explain Error](https://agentscamp.com/commands/analyze/explain-error.md): Diagnose an error message or stack trace and propose a fix.
- [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.
- [Add Docstrings](https://agentscamp.com/commands/docs/add-docstrings.md): Add or improve docstrings for the public API of a file or symbol.
- [Explain Code](https://agentscamp.com/commands/docs/explain-code.md): Explain what the given code does, in clear prose with a short summary.
- [Update README](https://agentscamp.com/commands/docs/update-readme.md): Update the README to reflect the current scripts, structure, and features of the repo.
- [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.
- [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.
- [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.
- [Plan Feature](https://agentscamp.com/commands/plan/plan-feature.md): Explore the codebase and produce an implementation plan for a feature.
- [Extract Function](https://agentscamp.com/commands/refactor/extract-function.md): Extract a code region into a well-named function and update the call site.
- [Refactor](https://agentscamp.com/commands/refactor/refactor.md): Refactor the target for readability and structure without changing behavior.
- [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.
- [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.
- [Security Scan](https://agentscamp.com/commands/review/security-scan.md): Scan the current diff or given paths for security vulnerabilities.
- [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 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.
- [Fix Failing Test](https://agentscamp.com/commands/testing/fix-failing-test.md): Diagnose and fix a failing test by finding the real root cause.
- [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.

## 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