SKILL.md files package domain expertise into something any AI agent can use. Drop one into your project and your agent learns how to process PDFs, design interfaces, write tests, or whatever the skill teaches.
113 skills
English | 简体中文 🧠 The data layer for AI systems. Skill Seekers turns any documentation, GitHub repo, or PDF into structured knowledge assets—ready to power AI Skills (Claude, Gemini, OpenAI), RAG pipelines (LangChain, LlamaIndex, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline) in minutes, not hours. Skill Seekers is the universal preprocessing layer that sits between raw documentatio
Practitioner-focused workflows for running, validating, and packaging reproducible open AI governance studies. This repository contains execution pipelines, source artifacts, and validation gates for reproducible governance reports. Every headline finding is expected to map to: - a versioned artifact - a deterministic query in the claims ledger - a reproducible execution path in pipelines/ Use thi
audit-prompt-caching is a portable Codex/agent skill for finding why LLM cache reuse fails across the request path: prompt/prefix caches, provider cache telemetry, cache-aware routing, agent tool stability, Bedrock checkpoints, OpenRouter routing drift, provider migration risk, and vLLM/SGLang KV reuse. LLM cache reuse usually fails silently. A timestamp in the system prompt, shuffled tool schemas
Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.
RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers. - Ingests the official MCP Registry, enriches records for RAG use-cases, and serves a subregistry API. - Exposes an MCP server (remote Streamable HTTP + local stdio) so agents can search/filter RAG MCP servers. MapRag is a discovery + routing layer for retrieval. It helps agents and humans
You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
aka Vibe Debugging This is an MCP Server and VS Code extension which enables claude to interactively debug and evaluate expressions. That means it should also work with other models / clients etc. but I only demonstrate it with Claude Desktop and Continue here. It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode. 1. Download the extension from re
Please migrate to our new KnowledgeBase MCP Server, which provides enhanced capabilities and improved accuracy. Model Context Protocol (MCP) is a standardized protocol designed to manage context between large language models (LLMs) and external systems. The Chargebee MCP Server offers a robust set of tools to improve developer efficiency. It integrates with AI-powered code editors like Cursor, Win
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
CentralMind Gateway: Create API or MCP Server in Minutes 🚀 Interactive Demo avialable here: https://centralmind.ai Simple way to expose your database to AI-Agent via MCP or OpenAPI 3.1 protocols. This will run for you an API: Which you can use inside your AI Agent: Gateway will generate AI optimized API. AI agents and LLM-powered applications need fas
Semantic search, similar content discovery, and structured research using Exa API
- MCP Server: code-to-tree - Using code-to-tree - Configure MCP Clients - Building (Windows) - Building (macOS) The code-to-tree server's goals are: 1. Give LLMs the capability of accurately converting source code into AST(Abstract Syntax Tree), regardless of language. 2. One standalone binary should be everything the MCP client needs. These goals imply: 1. The underlying syntax parser should be v
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
Stop copy-pasting between AI models. Roundtable AI is a local MCP server that lets your primary AI assistant delegate tasks to specialized models like Gemini, Claude, Codex, and Cursor. Solve complex engineering problems in parallel, directly from your IDE. Key Features: - Context Continuity: Shared project context across all sub-agents - Parallel Execution: All agents work simultaneously - Model
A Mattermost integration that connects to Model Context Protocol (MCP) servers, leveraging a LangGraph-based AI agent to provide an intelligent interface for interacting with users and executing tools directly within Mattermost. - 🤖 Langgraph Agent Integration: Uses a LangGraph agent to understand user requests and orchestrate responses. - 🔌 MCP Server Integration: Connects to multiple MCP serve
A tool similar to cloc, sloccount and tokei. For counting the lines of code, blank lines, comment lines, and physical lines of source code in many programming languages. Goal is to be the fastest code counter possible, but also perform COCOMO calculation like sloccount, LOCOMO estimation for LLM-based development costs, estimate code complexity similar to cyclomatic complexity calculators and prod
Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
Discover, compare, and run AI models using Replicate's API
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa