Find the perfect capability for your agent.
Creates Agent Skills for Claude Code following official standards. Use when the user wants to create a Skill, teach Claude new capabilities, add specialized knowledge, or create reusable instructions. Keywords: create skill, new skill, teach Claude, add capability, skill creation.
Discovers all Claude Code agents in the system including built-in, plugin, project, and user-level agents. Use when you need to find which agents are available, understand the agent ecosystem, or prepare agents for Actoris registration.
Generates module README.md with exercise planning table for CopilotTraining workshop modules. Researches URLs to understand new capabilities and creates strategic content outline. Use exercise-author skill to implement individual exercises.
Expert guidance for building conversational AI applications with Chainlit framework in Python. Use when (1) creating chat interfaces for LLM applications, (2) building apps with OpenAI, LangChain, LlamaIndex, or Mistral AI, (3) implementing streaming responses, (4) adding UI elements like images, files, charts, (5) handling user file uploads, (6) implementing authentication (OAuth, password), (7) creating multi-step workflows with visible steps, (8) building RAG applications with document upload, or (9) deploying chat apps to web, Slack, Discord, or Teams.
このスキルは外部のAI エージェントを実行するためのスキルです。 実装や調査の作業を別のAIエージェントに委譲してください。 あなた（メインのClaude）はユーザーとの対話・設計判断・方針決定に専念し、 実際の調査や実装はこのスキルに任せることでトークンとコンテキストを大幅に節約できます。 重要：実装や調査タスクが発生したら、詳細を考える前にまずこのスキルを使ってください。 実装の細かいやり取りをメインの会話で行うのはトークンの無駄になります。 また、あなたが中間に挟まることで、文脈を省略しがちな人間とタスク実行者との間での口論を防ぐこともできます。
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Multi-model adversarial debate where Claude defends positions against external CLI models (Gemini, Codex, Qwen). Use when facing complex decisions, wanting to expose blind spots, or needing documented reasoning for tradeoffs. Auto-triggers: "debate this", "challenge my thinking", "what am I missing", "devil's advocate", "stress test this idea", "poke holes", "second opinion", "validate this decision", "am I wrong about"
Enterprise Mermaid diagramming skill for Claude Code using MCP Playwright
Implementing human override capabilities for AI decisions with proper tracking, auditing, and feedback loops.
Verify generated specification documents for context preservation and quality issues. Automatically triggered after document generation. Checks that upstream requirements are preserved and identifies common specification problems.
Build FastAPI backend for OpenAI ChatKit with SSE streaming, conversation persistence, and AI agent integration. Handles /chatkit endpoint, ChatKit-compatible SSE format, conversation models, and message storage. Use when implementing chat backend, SSE streaming endpoint, or connecting AI agent to ChatKit frontend.
Build AI agents with the Claude Agent SDK (TypeScript/Python). Covers creating agents, custom tools, hooks, subagents, MCP integration, permissions, sessions, and deployment. Use when building, reviewing, debugging, or deploying SDK-based agents. Invoke PROACTIVELY when user mentions Agent SDK, claude-agent-sdk, ClaudeSDKClient, query(), or building autonomous agents.
Prompt Engineering Instructional Language (PEIL) - generates optimised system prompts for AI agents and LLMs. Use when creating agent system prompts, improving prompt quality, applying research-backed prompting techniques, or structuring prompts with role, context, instructions, and desired output. Keywords: prompt engineering, system prompt, agent prompt, LLM optimization, prompt generation.
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Create, evaluate, and improve Agent skills to production quality (100/100). Use when the user wants to create a new skill, review an existing skill, score a skill against best practices, or improve a skill's quality. Also use when the user mentions skill development, skill templates, or skill optimization.
Complete SDK for controlling Reachy Mini robot - head movement, antennas, camera, audio, motion recording/playback. Covers architecture (daemon/client), deployment modes (USB, wireless, simulation, on-Pi), and app distribution. Also includes advanced application patterns: MovementManager, layered motion, audio-reactive movement, face tracking, LLM tool systems, and OpenAI realtime integration. Use when: (1) Writing code to control Reachy Mini, (2) Moving the robot head or antennas, (3) Accessing camera/video, (4) Playing/recording audio, (5) Recording or playing back motions, (6) Looking at points in image or world space, (7) Understanding robot capabilities, (8) Connecting to real or simulated robot, (9) Building conversational AI apps, (10) Integrating with LLMs/OpenAI, (11) Deploying apps to robot, (12) Any robotics task with Reachy Mini.
