Agent Skills
Instruction packs that give your AI agent know-how. Three different kinds — pick the right one below.
✦ Standalone skills2,933
Self-contained. Install one into any project and it works on its own — no other software needed.
🧰 Tool add-ons725
Come bundled with a specific tool and only work together with it — they teach your agent how to operate that tool.
github-issues
✓★ 36,200by github
Create, update, and manage GitHub issues with full workflow support including types, labels, assignees, and dependencies. Supports issue creation with type categorization (Bug, Feature, Task, etc.), labels, assignees, and milestones via gh api REST calls Read operations via MCP tools for fetching issue details, comments, labels, and cross-repository search Handles issue updates (title, body, state, labels, assignees) and includes templates for bug reports, feature requests, and tasks...
microsoft-skill-creator
✓★ 36,200by github
Create hybrid agent skills for Microsoft technologies with local knowledge and dynamic Learn MCP lookups. Generates modular skill packages with frontmatter, reference documentation, and working code examples for any Microsoft technology (Azure, .NET, M365, Semantic Kernel, etc.) Uses three-phase investigation workflow: scope discovery via search, core content fetching, and depth exploration for best practices and troubleshooting Balances local storage of foundational concepts and common...
qdrant-search-quality
✓★ 36,200by github
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant…
langchain-rag
★ 846by langchain-ai
Complete RAG pipeline for document ingestion, embedding, retrieval, and LLM-powered response generation. Supports multiple document loaders (PDF, web pages, directories) and persistent vector stores (Chroma, FAISS, Pinecone) with configurable chunk size and overlap for optimal context preservation Includes similarity search, MMR (Maximal Marginal Relevance) retrieval, and metadata filtering to balance relevance and diversity in results Works with OpenAI embeddings and integrates seamlessly...
redis-development
✓★ 82by redis
Redis performance optimization across data structures, query engines, vector search, and semantic caching. Covers 29 prioritized rules across 11 categories including data modeling, memory management, Redis Query Engine (RQE), vector search with RedisVL, and LangCache semantic caching Provides structured guidance for connection pooling, JSON document handling, streams, clustering, security (ACLs), and observability Each rule includes explanations, correct examples, anti-patterns or usage...