Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
ruvectorgraph-node
by ricableNative Node.js graph database bindings with hypergraph support, Cypher queries, and persistence. Use when the user needs a graph database in Node.js, Cypher query execution, vertex/edge CRUD operations, graph traversals, shortest path algorithms, or hypergraph data modeling.
agentdb
by ricableRuVector-powered graph database CLI with Cypher queries, hyperedges, ACID persistence, and 150x faster vector search. Use when managing graph data stores, running Cypher queries, performing vector similarity search, managing database schemas, or building knowledge graphs for AI agents.
ruvector-nervous-system-wasm
by ricableBio-inspired AI components in WASM: Hyperdimensional Computing, BTSP synaptic plasticity, and neuromorphic spiking networks. Use when building brain-inspired classifiers, implementing one-shot learning with HDC, or simulating spiking neural networks in browsers.
ruvectoredge-net
by ricableDistributed compute network with WASM cryptographic security for edge AI coordination. Use when the user needs to build distributed peer-to-peer compute networks, coordinate edge AI nodes, implement secure mesh networking, or distribute workloads across browser and edge nodes.
ruvectoredge
by ricableEdge AI swarms for browsers with P2P networking, vector search, and neural networks. Use when the user needs browser-based AI swarms, peer-to-peer vector search, client-side neural network inference, decentralized agent coordination, or edge-deployed AI workloads without server infrastructure.
agentic-flow
by ricableAI agent orchestration platform with 66 specialized agents, MCP tool integration, multi-provider LLM support, and federation. Use when running AI agents with task descriptions, configuring MCP servers, managing agent federations, proxying Claude Code or Cursor, or spawning QUIC transport for low-latency agent communication.
ruvector-onnx-embeddings-wasm
by ricablePortable WASM embedding generation using ONNX Runtime with SIMD acceleration and parallel workers. Use when generating text embeddings in browsers without a server, running embedding models at the edge, or building offline-capable semantic search applications.
ruvector-scipix
by ricableOCR client for scientific documents - extracts LaTeX, MathML, and structured text from equations, papers, and technical diagrams. Use when parsing mathematical equations from images, extracting formulas from research PDFs, or converting scientific figures to structured data.
neural-tradermcp
by ricableMCP server exposing 87+ neural trading tools for AI agent integration. Use when connecting trading capabilities to Claude or other AI agents via MCP, exposing market data and strategy tools through Model Context Protocol, or building agent-driven trading automation.
elex-ran-features
by ricableEricsson LTE/NR RAN feature knowledge base. Query by acronym (IFLB, DUAC, MSM), FAJ/CXC codes, parameter names, counter patterns, or Boolean keywords. Returns feature descriptions, parameters, counters, KPIs, engineering guidelines, activation procedures, and dependencies. Supports cmedit command generation, dependency visualization, feature validation, and deployment scripts.
ml-paper-writing
by ricableWrite publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.