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 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.

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 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 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.

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 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.

LangChain Exa integration — semantic web search with ExaSearchRetriever (RAG), ExaSearchResults (agent tool), and ExaFindSimilarResults (find similar URLs). Unique features: use_autoprompt (LLM query rewriting), highlights (excerpts), summary (per-result LLM summaries), livecrawl (real-time), and date filtering.

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

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Google Search structured data implementation - Schema.org markup for rich results, JSON-LD templates, JavaScript generation, and SEO best practices

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Create repositories, commit code, push branches, open pull requests, manage issues, create and claim bounties, delegate agent tasks, and interact with the Base L2 name registry on the gitlawb decentralized git network. Use this skill when asked to create a repo, push code, open a PR, review code, merge a pull request, post or claim a bounty, register a name on Base L2, or delegate tasks to other agents on gitlawb. Do NOT use for GitHub, GitLab, or other centralized git hosts.

GitHub Pages static site hosting - setup, configuration, custom domains, Jekyll, and deployment

Load GitHub Actions automation workflows documentation including CI/CD pipelines, security scanning, and maintenance automation

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

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Use when creating or modifying Model Context Protocol (MCP) servers with FastMCP framework - guides through tools, resources, prompts, authentication, Claude Desktop integration, and production deployment with Python and TypeScript examples

Braiins Farm Monitor - Bitcoin mining fleet monitoring and management

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Entry-point skill for using claude-mem to keep CLAUDE.md and MEMORY.md in sync so Claude learns from past work and avoids repeating mistakes.