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Found 7,377 Skills
Expert knowledge of GitHub Copilot CLI - installation, configuration, usage, MCP servers, skills, custom agents, and troubleshooting. Use when asking about copilot cli, installing copilot, gh copilot, copilot commands, MCP setup, or copilot extensibility.
6-phase investigation workflow for understanding existing systems. Auto-activates for research tasks. Optimized for exploration and understanding, not implementation. Includes parallel agent deployment for efficient deep dives and automatic knowledge capture to prevent repeat investigations.
Expert knowledge of GitHub Copilot CLI - installation, configuration, usage, custom agents, MCP servers, and version management. Use when asking about copilot cli, copilot commands, installing copilot, updating copilot, copilot features.
Run Microsoft's eval-recipes benchmarks to validate amplihack improvements against baseline agents. Auto-activates when testing improvements, running evals, or benchmarking changes.
AG-UI (Agent-User Interaction) protocol reference for building AI agent frontends. Use when implementing AG-UI events (RUN_STARTED, TEXT_MESSAGE_*, TOOL_CALL_*, STATE_*), building agents that communicate with frontends, implementing streaming responses, state management with snapshots/deltas, tool call lifecycles, or debugging AG-UI event flows.
Coordinate multi-agent code review with specialized perspectives. Use when conducting code reviews, analyzing PRs, evaluating staged changes, or reviewing specific files. Handles security, performance, quality, and test coverage analysis with confidence scoring and actionable recommendations.
[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.
Design effective system prompts for custom agents. Use when creating agent system prompts, defining agent identity and rules, or designing high-impact prompts that shape agent behavior.
LangGraph state-machine design and debugging for `StateGraph`, node/edge routing, checkpoints, `interrupt`, and HITL flows. Use when building or troubleshooting graph-based agents with conditional edges and thread state.
Generate a plan for how an agent should accomplish a complex coding task. Use when a user asks for a plan, and optionally when they want to save, find, read, update, or delete plan files in $CODEX_HOME/plans (default ~/.codex/plans).
LangChain workflows for `create_agent`, LCEL chains, `bind_tools`, middleware, and structured output with production-safe orchestration. Use when implementing or refactoring LangChain application logic in Python or TypeScript.
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.