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.
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vibe-research
by ZhangyanboSet up and maintain a Vibe Research project with bi-directional sync between experimental code, paper.md, and developer docs. Use when starting a research project, managing experimental code, maintaining research documentation, creating academic papers, writing research logs, updating project docs, migrating an existing project to Vibe Research structure, or when the user mentions vibe research, paper sync, research log, experiment tracking, project migration, partial migration, incremental migration, doc update, or bi-directional sync between code and paper.
vibe-paper-writing
by ZhangyanboGuide for integrating user-provided materials (chat logs, documents, citations) into academic papers with proper scholarly style. Use when the user wants to incorporate chat/email threads, personal documents, or references into a LaTeX paper, or asks for academic writing assistance that requires careful source attribution and style preservation.
modeling-thinking
by ZhangyanboStructured multi-model analysis for complex non-mathematical, non-programming problems. Use when the user explicitly asks to "model", "analyze with models", or "use modeling thinking". Also offer to use this skill (wait for confirmation) when encountering: complex decisions with many interacting factors, strategic planning, social or organizational dynamics, prediction problems in ambiguous domains, or any "what should I do?" that requires reasoning through system-level effects. Do NOT invoke for mathematical (e.g., "solve this equation") or programming tasks — those are already well-served without this skill. For borderline cases (e.g., a life decision that might not need modeling), ask the user first. Never invoke silently. If you think this skill applies, say: "This seems like a good candidate for structured modeling — want me to approach it that way?"
alpaca-python-trading-coder
by ZhangyanboComprehensive Python programming skill for Alpaca Trading API (alpaca-py SDK + REST OpenAPI). Use when Codex needs to write, review, or refactor Python trading integration code for Alpaca accounts, orders, positions, portfolio history, watchlists, assets, options, corporate actions, account activities, market clock/calendar, crypto funding, or perpetual funding endpoints. Includes typed input parameters, per-API Python examples, output structure types, and SDK-to-REST mapping. This skill is for code authoring and analysis, not autonomous trade execution.
alpaca-live-trade
by ZhangyanboExecute trading commands on Alpaca (stocks, options, crypto) via curl or alpaca-py SDK. Covers account info, orders (market/limit/stop/bracket/mleg), positions, assets, options contracts, watchlists, portfolio history, account activities, calendar/clock, corporate actions, and crypto wallets. Use when the user asks to place trades, check positions, manage orders, query account status, or perform any Alpaca Trading API operation.
agentmail-sdk-integration
by ZhangyanboIntegrate AgentMail into Python or Node.js applications with accurate API/SDK interfaces, input-output types, and data structures. Use when implementing inbox/message/thread/draft/domain/webhook/websocket/metrics/pod workflows, generating AgentMail client code, reviewing AgentMail integration correctness, or mapping business requirements to concrete AgentMail endpoints.
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.