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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yield-curve-regime-detector
by yuping322Assess yield curve shape, inversion signals, term premiums, and macro implications. Use when the user asks about the yield curve, inversion, or rate-regime impacts.
findata-toolkit-us
by yuping322Financial data toolkit for US market analysis. Provides scripts to fetch real-time stock data (yfinance), SEC filings and insider trades (EDGAR), financial statement calculators (DuPont, Z-Score, M-Score, F-Score), portfolio analytics (VaR, stress testing, health scoring), multi-factor screening, and macro indicators (FRED). Use when you need live US market data to ground investment analysis. All data sources are free — no API keys required.
bse-selection-analyzer
by yuping322扫描与分析北交所（BSE）标的，按流动性、成长性、行业景气与“专精特新”特征输出候选清单，并给出流动性与波动风险提示。当用户询问北交所精选、北交所选股、专精特新筛选、或需要北交所板块研究时使用。
hk-concentration-risk
by yuping322港股集中度风险监控器。监控投资组合的集中度风险、行业集中度、个股集中度等。用于管理投资组合的集中度风险。
hk-currency-risk
by yuping322港股汇率风险监控器。监控港币汇率波动、汇率风险敞口、汇率对冲策略等。用于管理港股投资的汇率风险。
hk-dividend-tracker
by yuping322港股股息跟踪器。监控港股公司股息政策、分红历史、股息收益率等。用于收益投资和股息策略分析。
hk-etf-flow
by yuping322港股ETF资金流向分析器。专门分析港股ETF的资金流向、持仓变化、溢价折价等。用于把握ETF投资机会和资金动向。
hk-financial-statement
by yuping322港股财务报表分析器。提供港股公司财务报表分析、财务比率计算、财务健康度评估等功能。用于价值投资和基本面分析。
hk-foreign-flow
by yuping322港股外资流向分析器。专门分析外资通过港股通、QFII、直接投资等方式流入流出港股的资金动向。用于把握国际资金动向和投资机会。
hk-liquidity-risk
by yuping322港股流动性风险监控器。监控港股市场流动性状况、个股流动性风险、市场深度分析等。用于识别流动性风险和制定交易策略。
hk-market-breadth
by yuping322港股市场广度监控器。监控港股市场广度指标、上涨下跌家数、新高新低比率等。用于评估市场整体健康状况和趋势强度。
hk-market-overview
by yuping322港股市场概览分析器。提供港股市场整体表现、主要指数、板块轮动、市场情绪等综合分析。用于快速了解港股市场整体状况和趋势。
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.