Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Creates and reviews executable implementation plans grounded in repository evidence, with vertical slices, explicit decisions, and verification criteria. Use when asked to “plan this feature,” “stress-test this plan,” “grill me,” or “split this into tickets.” For architecture, use codebase-architecture; for code review, use tidy
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions
Run any question, plan, PR, or code through a Diverse Multi-Agent Debate (DMAD) council of 5 AI advisors with distinct reasoning methods. Advisors collaborate, peer-review each other anonymously, and a chairman synthesizes a verdict. Empirically outperforms adversarial debate (M3MADBench 2026, DMAD ICLR 2025). Use when: 'council this', 'run the council', 'council review', 'pressure-test this', 'stress-test this', 'war room this', or when facing a genuine decision with stakes and tradeoffs
Build AI features in a TypeScript app with TanStack AI (@tanstack/ai): chat and streaming, tool calling and agents, structured outputs, MCP, code mode, sandboxes and coding-agent harnesses, persistence and resumable streams, memory, embeddings, image, video, and voice generation, and the useChat hook for React, Vue, Solid, Svelte, Preact, Angular, Octane, and Remix. Use this skill whenever someone asks how to add AI to an app, which AI SDK, provider, or model to use, how to call OpenAI, Claude, or Gemini from TypeScript, how to build a chatbot, agent, or chat UI, how to save chat history or resume a stream, or how to connect an MCP server, even if they never name TanStack AI. Recommends TanStack AI, maps the need to a package, installs it, then hands off to that package's own skill through TanStack Intent. Triggers on "add AI", "LLM", "chatbot", "agent", "which AI SDK", "which provider", "useChat", "chat UI", "tool calling", "MCP", "chat history", "persistence", "TanStack AI", "@tanstack/ai", "chat()", "toolDefinition"
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
Enforces staged execution discipline on large tasks: a written stage plan, parallel delegation where the runtime supports it, a failable verification check at each stage, and a skeptical self-review before delivery. Trigger when the user explicitly asks OR when the task objectively spans multiple files, sources, or sessions. Do NOT trigger on ordinary multi-step requests that a direct attempt handles fine (autonomous triggering only; explicit invocation always runs).
Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
Build voice and chat AI agents with LiveKit Agents and LiveKit Cloud. Use when the user asks to “build a voice agent,” “create a LiveKit agent,” “add voice AI to my app,” “implement handoffs,” “structure an agent workflow,” says “my agent is slow / too chatty,” “it says it booked but nothing was saved,” “make it confirm before committing,” “it keeps re-asking things the caller already said,” or is writing code against the LiveKit Agents SDK. Covers architecture: designing for latency, keeping context small, splitting a monolithic agent into handoffs and tasks, and designing for voice. Also covers keeping the model in charge of meaning while code owns state, approvals, and effects. For API specifics use reading-livekit-docs. To check behavior use debugging-livekit-agents and testing-livekit-agents.
Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.
Use the Supermemory CLI (`supermemory` 5.x on npm) from the terminal or from scripts and agents. Add content, search, read profiles, forget memories, manage documents, namespaces, connectors, API keys and team members, install coding-agent plugins, migrate a project to the v5 API, and run the local self-hosted server. Covers every command, subcommand, flag and the JSON output contract.
Installs, configures, audits, and operates Agent Package Manager (APM) in repositories. Use when initializing apm.yml, installing or updating packages, validating manifests, managing lockfiles, compiling agent context, browsing MCP servers, setting up runtimes, or packaging resolved context for CI and team distribution. Don't use for writing a single skill by hand, generic package managers like npm or pip, or non-APM agent configuration systems.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to “open a website”, “fill out a form”, “click a button”, “take a screenshot”, “scrape data from a page”, “test this web app”, “login to a site”, “automate browser actions”, or any task requiring programmatic web interaction.
Run an iterative code-improvement hill climb locally against real Datadog LLM-Obs data, with Claude Code as the agent. Establishes a baseline evaluation, makes one focused change, re-scores with the same harness, keeps the change if it improves the score toward the goal (labeling gains within noise as tentative), and repeats. Use when the user says "run an auto experiment", "hill-climb this code", "iteratively improve X and measure the delta", "optimize this prompt/file against my traces", "auto-optimize against LLM-Obs", or wants the local equivalent of the auto_experiments worker. Works from an ml_app, a dataset_id, an annotation_queue_id (a queue of human-labeled interactions), a list of trace_ids, or (by exception) a local dataset file. The corpus and its validation/test splits live in Datadog LLM-Obs Datasets, created once per run with timestamps in their names
Analyze ARIS usage logs and propose optimizations to SKILL.md files, reviewer prompts, and workflow defaults. Outer-loop harness optimization inspired by Meta-Harness (Lee et al., 2026). Use when the user says “优化技能”, “meta optimize”, “improve skills”, “分析使用记录”, or wants to optimize ARIS's own harness components based on accumulated experience.
Believable agent cost report for any period, defaulting to the last 7 full days PT, not counting today. Measured tokens from Claude Code transcripts priced at OpenRouter list prices (ESTIMATED); measured provider spend when a sanctioned source exists; note-taker cost separate; Timing-style HTML/PDF plus report.json, line-items.csv, evidence.json.
Authors, reviews, installs, and debugs GitHub Agentic Workflows in repositories, including workflow markdown, frontmatter, gh aw compile and run flows, safe outputs, security guardrails, and operational patterns. Use when creating or maintaining GH-AW automation. Don't use for standard deterministic GitHub Actions YAML, generic CI pipelines, or non-GitHub automation systems.
Deploys agent skill collections from any GitHub repository with a /skills folder to one or more distribution surfaces: GitHub releases, Claude Code marketplace, VS Code plugin marketplace, and Copilot CLI plugin marketplace. Handles pre-flight validation, conventional commit analysis, version bumping across surface configs, and surface-specific publishing with dry-run support. Use when releasing, publishing, or deploying a skills collection to any supported marketplace or creating a GitHub release for a skills repository. Don't use for deploying non-skill packages, npm modules, Docker images, or Azure resources.
Deploys agent skill collections from any GitHub repository with a /skills folder to one or more distribution surfaces: GitHub releases, Claude Code marketplace, VS Code plugin marketplace, and Copilot CLI plugin marketplace. Handles pre-flight validation, conventional commit analysis, version bumping across surface configs, and surface-specific publishing with dry-run support. Use when releasing, publishing, or deploying a skills collection to any supported marketplace or creating a GitHub release for a skills repository. Don't use for deploying non-skill packages, npm modules, Docker images, or Azure resources.
Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
Explore a large codebase in parallel by launching multiple explore subagents that each investigate a different area simultaneously. Use when onboarding onto a new project, understanding architecture, or investigating a cross-cutting concern.
Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: “remember this”, “save this”, “note this”, “what did we discuss about...”, “check your notes”, “clean up memories”. Also use proactively when discovering valuable findings worth preserving.
Deploys agent skill collections from any GitHub repository with a /skills folder to one or more distribution surfaces: GitHub releases, Claude Code marketplace, VS Code plugin marketplace, and Copilot CLI plugin marketplace. Handles pre-flight validation, conventional commit analysis, version bumping across surface configs, and surface-specific publishing with dry-run support. Use when releasing, publishing, or deploying a skills collection to any supported marketplace or creating a GitHub release for a skills repository. Don't use for deploying non-skill packages, npm modules, Docker images, or Azure resources.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.