Agent skills

AI & Agents skills

Read straight from the source repositories, not from submitted listings. Every skill shows what it does, what is inside, where it came from — and whether attention around its source is actually growing.

Toolclaude-code 29,140codex 4,755cursor 3,111copilot 976windsurf 55cline 34
CategoryWorkflow & Productivity 4,979AI & Agents 3,037Data & Analytics 2,345Code Review & Quality 1,376Backend & API 1,244Security 1,194Design & Presentation 1,154Documentation 965Content & Marketing 916Testing & QA 777DevOps & Cloud 576Databases 550Frontend 469Business & Finance 328Media & Video 257Other 9,833
4,965 found
2,161–2,208 · page 46 / 104
pydanticai-docsUse this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured…majiayu000pydanticai-docsUse this skill for requests related to Pydantic AI framework - building agents, tools, dependencies, structured outputs, and…majiayu000pyfixest-grid-shardingDiagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite…kennethkhoocypymc-modelingBayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference…majiayu000python-agent-developmentPython Agent 开发规范（Windows wxauto v4），包括项目结构、模块化、wxauto 使用、IPC 集成、错误处理、测试和部署。majiayu000python-performance-optimizationProfile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python…sickn33pytorch-cudaConfigure and verify CUDA 13 readiness (toolkit, driver, and PyTorch wheel support), then run PyTorch CUDA with reliable timing…majiayu000pytorch-model-cliGuidance for implementing CLI tools that perform inference using PyTorch models in native languages (C/C++/Rust). This skill…majiayu000pytorch-model-trainerBuild pytorch model trainer operations. Auto-activating skill for ML Training. Triggers on: pytorch model trainer, pytorch model…majiayu000writespytorch-trainerPyTorch model training skill with custom training loops, gradient management, and GPU optimization.a5c-aiwritespytorch-trainerPyTorch model training skill with custom training loops, gradient management, and GPU optimization.majiayu000writesqdrant-memoryUse this skill for semantic search, long-term memory storage, and RAG (Retrieval Augmented Generation). Enables vector-based…majiayu000qdrant-sparseQdrant sparse vector operations: collection creation with SparseVectorParams, Modifier.IDF for miniCOIL/SPLADE/BM42, upserting…majiayu000quantizationModel quantization for efficient inference and training. Covers precision types (FP32, FP16, BF16, INT8, INT4), BitsAndBytes…majiayu000quick-analyzer-agentFast ticker analysis for /analysis page. Provides quick BUY/SELL/HOLD recommendations based on technical indicators, recent news…majiayu000quick-researchThis skill should be used when users need comprehensive research on a topic requiring exploration of multiple sources, synthesis…majiayu000zenml-quick-winsImplements ZenML quick wins to enhance MLOps workflows. Investigates codebase and stack configuration, recommends high-priority…majiayu000qwen-coderProvides Qwen Coder CLI delegation workflows for coding tasks using Qwen2.5-Coder and QwQ models, including English prompt…majiayu000writesQwen-OllamaUsing Qwen 2.5 models via Ollama for local LLM inference, text analysis, and AI-powered automationmajiayu000RAG PipelineDetails on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.majiayu000ragImplements Retrieval-Augmented Generation for AI models to fetch and use external knowledge.majiayu000rag-architectUse when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document…majiayu000rag-architectUse when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document…majiayu000rag-architectUse when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document…majiayu000rag-chunking-strategyDocument chunking with multiple strategies including semantic, recursive, and fixed-size chunkinga5c-aiwritesrag-curatorCurador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade.majiayu000rag-embedding-generationBatch embedding generation with caching, rate limiting, and multiple provider supporta5c-aiwritesrag-engineerExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and…majiayu000rag-engineerExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and…majiayu000rag-engineerExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and…majiayu000rag-engineerExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and…majiayu000rag-engineerExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and…majiayu000RAG ExpertExpert in Retrieval-Augmented Generation systems - knowledge bases, chunking strategies, embedding optimization, and production…majiayu000ragImplements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation…majiayu000writesrag-hybrid-searchHybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion…a5c-aiwritesrag-hybrid-searchHybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion…majiayu000writesrag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000Rag ImplementationComprehensive guide for Retrieval-Augmented Generation (RAG) implementation using LangChain. This skill covers the complete RAG…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…wshobsonrag-implementationBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when…majiayu000rag-infrastructureBuild and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search.…majiayu000ragUse when building Retrieval-Augmented Generation systems - covers document ingestion, hybrid search retrieval, reranking results…majiayu000
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How the catalog works
What is an agent skill?

A folder with a SKILL.md inside — instructions, and often scripts and assets, that an AI agent loads when the task matches. Claude Code, Codex, Cursor and Copilot all read the same format, so one skill usually works across them.

Where does this catalog come from?

We read 660 source repositories straight from their file trees rather than from submitted listings — what you see is what is actually published. 98 repositories were rejected because they advertise skills but contain none: link lists, not folders.

Why is there no install counter?

Because install counts live in the registry that serves `npx skills add`, and that is not ours — publishing a number we cannot verify would be worse than showing none. Instead we show where a skill comes from and whether attention around its source is actually growing, measured from our own weekly snapshots.

Do you deduplicate?

Yes, and it matters more than expected. Aggregator repositories republish the same skill in several places — one source carried 6,317 SKILL.md files for 2,001 actual skills. We collapse by folder name and keep the canonical copy, so the catalog counts things, not copies.

Keep going
Collections, ranked →
Which skill collections are moving this week, from our own snapshots.
Open Source Radar →
The repositories behind these skills, watched daily.
The makers →
Who publishes the skills you install.
Startup rankings →
The companies building in the same ecosystem.