ha-entity-architecture
Layered Home Assistant custom entity architecture—CoordinatorEntity bases, platform/category bases, registry-only platform files, translation-backed naming, stable IDs, and options reload wiring.
Layered Home Assistant custom entity architecture—CoordinatorEntity bases, platform/category bases, registry-only platform files, translation-backed naming, stable IDs, and options reload wiring.
Use when optimizing CLAUDE.md, AGENTS.md, custom commands, or skill files for Claude 4.5 models - applies documented Anthropic best practices systematically instead of inventing improvements
Understand agent context isolation and write effective prompts for spawned agents. Use when orchestrating multi-agent workflows to ensure subagents receive complete, self-contained context.
Full SDK integration test that runs actual queries through the Claude SDK sandbox. Use after making changes to SDK client code, session management, skill loading, network proxy, voice/TTS, or image generation. Runs real prompts through the SDK to verify the complete path works.
Cost-first delegation patterns and decision frameworks for multi-AI coordination
**REQUIRED for ALL LimaCharlie operations** - list orgs, sensors, rules, detections, queries, and 179 functions. NEVER call LimaCharlie MCP tools directly. Use cases: 'what orgs do I have', 'list sensors', 'search IOCs', 'run LCQL query', 'create detection rule'. This skill loads function docs and delegates to sub-agent.
Use when facing 3+ independent failures that can be investigated without shared state or dependencies - dispatches multiple Claude agents to investigate and fix independent problems concurrently
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
LLM deployment strategies including vLLM, TGI, and cloud inference endpoints.
Claude as intelligent orchestrator for multi-agent workflows. Coordinates specialized agents (Gemini, local models, tools) using Shell-As-Bus pattern with capability-based delegation.
This skill should be used when configuring or using the OpenCode CLI for headless LLM automation. Use when the user asks to "configure opencode", "use opencode cli", "set up opencode", "opencode run command", "opencode model selection", "opencode providers", "opencode vertex ai", "opencode mcp servers", "opencode ollama", "opencode local models", "opencode deepseek", "opencode kimi", "opencode mistral", "fallback cli tool", or "headless llm cli". Covers command syntax, provider configuration, Vertex AI setup, MCP servers, local models, cloud providers, and subprocess integration patterns.
Review aviation agent code changes for architecture compliance. Use when reviewing changes to shared/aviation_agent/, web/server/api/aviation_agent_chat.py, or configs/aviation_agent/. Verifies UI payload stability, tool name consistency, state management patterns, and LangGraph best practices.
Design framework-agnostic AI agents using Oracle's Open Agent Specification for portable, interoperable agentic systems with JSON/YAML definitions
Return to normal stop behavior. Use PROACTIVELY after completing all tasks in continuous work mode, when work is committed and tests pass, or when reaching natural stopping point. User can invoke with /claude-allow-stop.
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
AI agent development with LangChain, CrewAI, AutoGen, and tool integration patterns.
Protocol for discovering and coordinating with other AI agents in a federated communication channel
Phase execution protocol to prevent context drift during long development sessions. MUST be read at the start of each phase and referenced in the last TODO of each phase. This ensures AI maintains context across phases.
Converts text to speech audio using OpenAI TTS API. Use when users request audio versions of text or want responses read aloud.
When asked implementation questions or tool selection questions like "how do I implement", "what's the best way to", "how should I", "which library", "what tool should I use", or "how might we", search GitHub for prior art, code examples, and proven approaches before proposing solutions
효과적인 스킬 생성 가이드. 사용자가 Claude의 기능을 전문 지식, 워크플로우, 도구 통합으로 확장하는 새 스킬을 만들거나 기존 스킬을 업데이트하려 할 때 사용한다.
System 2 attention mechanisms for deliberate, slow reasoning in transformer
Distributed LLM inference across Apple Silicon clusters with exo. Run models across Mac Studios via Thunderbolt RDMA, auto peer discovery, and MLX sharding. Use for multi-device inference, model parallelism, or building LLM clusters.
This skill should be used when the user says "hello", "hi", or "greet me".