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
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
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
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
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks.
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.
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
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.
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
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Guide for upgrading Stripe API versions and SDKs
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.