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...
analyze-retail-inverse-etf-allocation
by fatfingererr以槓桿反向 ETF（做空）相對槓桿正向 ETF（做多）的交易占比，作為散戶風險偏好代理指標，評估 SPX 後續下行風險。
forecast-sector-relative-return-from-yield-spread
by fatfingererr用美債殖利率曲線利差（如 2Y-10Y）建立「領先關係」，推估未來一段時間內成長股（Nasdaq 100）相對防禦股（Healthcare/XLV）的相對績效方向與幅度。
analyze-jgb-insurer-superlong-flow
by fatfingererr從日本保險公司對超長期（10年以上）JGB 的淨買賣時間序列，自動產出「本月是否創紀錄淨賣出、連續淨賣出月數、期間累積淨賣出」等結論。
cost-density-net-rr-calculator
by fatfingererr計算交易成本對風險報酬比的非線性衰減影響。將固定佣金與點差整合為「成本密度」指標，揭示停損大小與策略效率的雙曲線關係，識別「獲利事件視界」閾值。
demographic-fiscal-trap-analyzer
by fatfingererr分析人口老化、債務動態、官僚膨脹與通膨稀釋交互作用下的「財政陷阱」風險，量化各國/地區的財政脆弱度並識別潛在的貨幣稀釋路徑
detect-atr-squeeze-regime
by fatfingererr以 14 日指數平滑 ATR 偵測市場是否從秩序型趨勢轉為波動主導的擠壓（squeeze）行情，並輸出對技術位、停損、交易持有期的可行性評估。
detect-palladium-lead-silver-turns
by fatfingererr以鈀金的先行轉向作為確認條件，檢驗白銀短期漲跌是否獲得工業景氣與風險情緒的同步支持，並標記缺乏鈀金參與度的失敗走勢。
detect-us-equity-valuation-percentile-extreme
by fatfingererr把多個股票估值指標統一轉成在過去百年歷史中的分位數，再合成一個總分，判斷目前是否處於歷史極端高估區間，並用歷史類比（如 1929、1965、1999）給出風險解讀。
monitor-etf-holdings-drawdown-risk
by fatfingererr偵測「商品價格上漲、但對應實物 ETF/信託持倉卻下滑」的背離現象，並用多指標交叉驗證，評估是否存在實物供給緊張/交割壓力風險。
nickel-concentration-risk-analyzer
by fatfingererr以全球鎳供給結構為核心，量化各國的主導程度（例如印尼）、主要礦區供給量、以及政策配額/減產情境對全球供需平衡與價格非對稱的影響。
detect-fed-unamortized-discount-pattern
by fatfingererr檢查聯準會持有證券的未攤銷折價（Unamortized Discounts）是否出現與特定歷史危機期間相似的走勢模板，並用多指標交叉驗證是否真的屬於「金融壓力升高」而非單純會計/利率效果。
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.