langgraph-patterns-expert
Build production-grade agentic workflows with LangGraph using graph-based orchestration, state machines, human-in-the-loop, and advanced control flow
Build production-grade agentic workflows with LangGraph using graph-based orchestration, state machines, human-in-the-loop, and advanced control flow
user-level指示の更新。Claudeが間違いを犯した際に、再発防止のためCLAUDE.md/context/を更新。`/update-inst <間違えた内容>` で使用。
Research-backed prompting techniques for improved AI response quality (+45-115% improvement). Use when optimizing prompts, enhancing agent instructions, or when maximum response quality is critical. Invoked by /ai-eng/optimize command. Includes expert persona, stakes language, step-by-step reasoning, challenge framing, and self-evaluation techniques.
微信小程序聊天工具开发指南。当开发聊天工具分包、配置 chatTools、发送消息到群聊、动态消息、获取群成员信息、wx.openChatTool、wx.getChatToolInfo 时使用。
Use when: (1) constructing prompts for subagents, (2) invoking the Task tool, or (3) writing/improving skill instructions or any LLM prompts
Use for atypically complex problems requiring explicit step-by-step reasoning. Skill autonomously decides if sequential-thinking MCP overhead is justified based on problem complexity.
Enterprise MCP (Model Context Protocol) server development using FastMCP 2.0 with production-grade tools, resources, prompts, and intelligent agent-first design. Use when building MCP servers, integrating with LLMs, creating agent tools, implementing RAG systems, or developing protocol-based AI integration solutions.
Generate llms.txt and llms-full.txt files for AI agent consumption following the llmstxt.org standard. Use when updating site content that should be reflected in the llms files, or when building/deploying the site.
ALWAYS invoke this skill FIRST at session start, after context reset/compaction, or when user mentions "reset", "new session", "where were we", "what was I working on". Do NOT skip this for specific task requests—orient first, then execute. This is Rule 0.
Natural language wrapper for checkpoint commands - automatically triggers /checkpoint:create, /checkpoint:restore, /checkpoint:list when users request checkpoint operations
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
Build AI agents with Google's Agent Development Kit (ADK) Python. Use when building AI agents with tool integration, multi-agent systems, workflow agents (sequential, parallel, loop), or deploying to Vertex AI.
**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
Audit AI systems for safety, bias, and responsible deployment
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.