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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qveris
by QVerisAIDiscover, inspect, and call third-party API capabilities via the QVeris MCP server, then generate production code that calls the QVeris REST API for tasks like fetching weather data, stock prices, or public datasets. Use when the user needs to find an external API, integrate a web service, connect to a third-party REST endpoint, or retrieve data from an external source.
qveris-cli
by QVerisAIUse QVeris CLI to discover and call third-party API tools. Use when you need to find an external API, integrate a web service, or retrieve live data (prices, weather, news, etc).
stock-copilot-pro
by QVerisAIOpenClaw stock analysis skill for US/HK/CN markets. Combines QVeris data sources (THS, Caidazi, Alpha Vantage, Finnhub, X sentiment) for quote, fundamentals, technicals, news radar, morning/evening brief, and actionable investment insights.
qveris-official
by QVerisAIQVeris is a capability discovery and tool calling engine. Use discover to find specialized API tools — real-time data, historical sequences, structured reports, web extraction, PDF workflows, media generation, OCR, TTS, translation, and more. Then call the selected tool. Discovery queries must be English API capability descriptions. Requires QVERIS_API_KEY.
qveris-cn
by QVerisAIQVeris is a capability discovery and tool calling engine. Use discover to find specialized API tools — real-time data, historical sequences, structured reports, web extraction, PDF workflows, media generation, OCR, TTS, translation, and more. Then call the selected tool. Discovery queries must be English API capability descriptions. Requires QVERIS_API_KEY.
chairman-daily-brief
by QVerisAIExclusive morning and evening briefing skill for listed company chairmen. Provides daily briefings from an executive decision-making perspective, covering stock price movements, industry policy updates, competitor intelligence, capital market sentiment, and regulatory announcement alerts. Morning briefings focus on pre-market outlook and risk warnings; evening briefings emphasize post-market review and next-day strategies. Data aggregated from multiple QVeris sources.
exchange-rate
by QVerisAIReal-time forex and cryptocurrency exchange rate lookup and amount conversion powered by QVeris. Supports multiple providers (Alpha Vantage, Twelve Data) with fallback for reliability.
x-founder-operations
by QVerisAISystematic X (Twitter) operations skill for founders, indie developers, and tech professionals. Implements a daily Plan-Do-Check-Act closed loop with content strategy (5-pillar system), multimodal creation, thread growth playbook, engagement and community building, product promotion integration, persona development, competitor analysis, and data-driven continuous improvement. Use when managing an X account, planning content, analyzing tweet performance, engaging with community, running competitive analysis, or optimizing posting strategy.
portfolio-quick-diagnosis
by QVerisAI用于快速分析投资组合的持仓概览、集中度检查和总体评价。适用于用户请求组合快速诊断、持仓概览、集中度分析或面向客户的结构化结果说明时。
portfolio-optimization
by QVerisAI用于基于约束条件生成投资组合优化处方。调用 prescription_main.py 完成量化优化，仅保留约束收集、异常处理和结果解释。
portfolio-deep-diagnosis
by QVerisAI用于对投资组合进行深度诊断。收集 4 个分析参数后调用 run_pipeline() 完成量化计算，再向用户解释诊断结果。
event-intelligence
by QVerisAI定时推送、手动获取事件资讯，并支持查询单条事件详情。
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.