Claude Command And Control

GitBook documentation platform. Use when creating, publishing, or managing docs sites — content structure, blocks, Git Sync, customization, AI search, collaboration, and the GitBook API.

Decentralized git for AI agents and humans. Use when the user wants to create repositories, push code, open pull requests, review and merge PRs, manage issues, create or claim bounties, delegate tasks to other agents, register human-readable names on Base L2, or interact with the gitlawb decentralized git network. Supports cryptographic DID identities, Ed25519-signed pushes, UCAN capability delegation, libp2p networking, and 31+ MCP tools for AI agent integration. Do NOT use for GitHub, GitLab, or other centralized git hosts.

LangChain Python package — new create_agent() factory (builds a LangGraph agent from a model string + tools + middleware), plus a comprehensive middleware system covering HITL, PII redaction, model fallback, rate limiting, auto-summarization, context editing, shell tools, and todo planning.

LangGraph (Python) — build stateful, controllable agent graphs with checkpointing, streaming, persistence, interrupts, fault tolerance, and durable execution. Covers both Graph API (StateGraph) and Functional API (@entrypoint/@task).

LangSmith Python SDK — trace, evaluate, and monitor LLM applications. Covers @traceable decorator, trace context manager, Client API, evaluate() / aevaluate(), comparative evaluation, custom evaluators, dataset management, prompt caching, ASGI middleware, and pytest plugin.

MemPalace local-first AI memory system. Use when setting up persistent memory for Claude Code sessions, mining project files or conversation transcripts, querying past context, configuring MCP tools, managing the knowledge graph, or troubleshooting palace operations.

LangChain AWS integration — ChatBedrockConverse (Claude/Nova/Llama/Mistral on Bedrock), BedrockEmbeddings, AmazonKnowledgeBasesRetriever, BedrockAgentsRunnable, BedrockRerank, BedrockPromptCachingMiddleware, CodeInterpreterToolkit, BrowserToolkit (computer use), Neptune graph chains, and SageMaker endpoint.

LangChain Neo4j integration — Neo4jGraph for Cypher queries and schema inspection, GraphCypherQAChain for natural-language-to-Cypher Q&A, Neo4jVector for vector/hybrid RAG, Neo4jSaver LangGraph checkpointer, Neo4jChatMessageHistory, and GraphDocument/Node/Relationship for knowledge graph construction.

LangChain Redis integration — RedisVectorStore for RAG, RedisCache and RedisSemanticCache for LLM response caching, RedisChatMessageHistory for persistent conversation memory, and RedisConfig for connection management. Requires Redis Stack (redis/redis-stack-server).

LangChain Ollama integration — run local LLMs with ChatOllama (chat completions, tool calling, structured output, reasoning/thinking mode), OllamaLLM (raw text completions), and OllamaEmbeddings. Connects to a local Ollama server at localhost:11434.

Professional technical writing and documentation skill for creating and maintaining comprehensive, accurate, and user-friendly documentation for codebases, APIs, deployment processes, and end-user guides.

LangChain PostgreSQL integration — PGVectorStore (v2, recommended) and PGVector (v1 legacy) for pgvector RAG, PostgresChatMessageHistory for persistent chat, HNSW/IVFFlat index management, hybrid search, async-first engine via PGEngine, and custom metadata columns.

LangGraph Graph API (Python) — build explicit DAG agent workflows with StateGraph, typed state, nodes, edges, Command routing, Send fan-out, checkpointers, interrupts, and streaming. Use when you need explicit control flow and graph topology.

LangChain Deep Agents (Python) — build, deploy, and customize stateful long-running agents with virtual filesystems, subagents, human-in-the-loop, and LangSmith observability. Also covers LangGraph, LangChain OSS chains/retrievers, and Agent Server API.

LangChain OpenRouter integration — ChatOpenRouter gives access to hundreds of models (Claude, GPT-4o, Gemini, Llama, etc.) through a single API key. Supports provider routing preferences, reasoning models, plugins, tool calling, structured output, and request attribution/tracing.

LangChain Perplexity AI integration — ChatPerplexity (chat model with built-in web search and date/domain filtering), PerplexitySearchRetriever for RAG, PerplexitySearchResults tool, PerplexityEmbeddings, and reasoning output parsers (ReasoningJsonOutputParser, strip_think_tags).

LangChain MCP Adapters — connect LangChain agents to MCP (Model Context Protocol) servers. Load MCP tools, prompts, and resources as LangChain-compatible objects. Supports stdio, SSE, StreamableHTTP, and WebSocket transports. Includes interceptors, callbacks, and multi-server management.

LangGraph Functional API (Python) — build stateful agent workflows with @entrypoint and @task decorators. Imperative Python style with LangGraph persistence, streaming, HITL, and durable execution. Ideal for wrapping existing agents (CrewAI, AutoGen, Strands) or complex parallel task logic.

Comprehensive best practices for Terraform infrastructure as code from Anton Babenko's community guide