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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etf-premium
by himself65Calculate ETF premium/discount vs NAV via Yahoo Finance, and decompose single-day surges into NAV-driven vs structural components (gamma squeeze, dealer hedging, blocked AP arbitrage). Use whenever the user asks about an ETF's premium or discount, NAV comparison, why an ETF diverged from its holdings, or how much of a move is dealer-hedging-driven. Triggers: "ETF premium", "ETF discount", "NAV premium", "is SPY at a premium", "BITO premium", "IBIT premium", "bond ETF discount", "trading above/below NAV", "ETF premium screener", "biggest discount", "compare ETF NAV", "ETF arbitrage", "ETF gamma squeeze", "ETF premium surge", "decompose ETF move", "dealer gamma exposure", "GEX for ETF", "why did this ETF jump", "premium convergence", "AP arbitrage blocked", or any request about the gap between an ETF's price and underlying value. Especially relevant for leveraged, inverse, international, bond, commodity, and crypto ETFs.
earnings-preview
by himself65Generate a pre-earnings briefing for any stock using Yahoo Finance data. Use this skill whenever the user wants to prepare for an upcoming earnings report, understand what analysts expect, review a company's beat/miss track record, or get a quick overview before an earnings call. Triggers include: "earnings preview for AAPL", "what to expect from TSLA earnings", "MSFT reports next week", "earnings preview", "pre-earnings analysis", "what are analysts expecting for NVDA", "earnings estimates for", "will GOOGL beat earnings", "earnings beat/miss history", "upcoming earnings", "before earnings", "earnings setup", "consensus estimates", "earnings whisper", "EPS expectations", "what's the street expecting", "earnings season preview", any mention of preparing for or previewing an earnings report, or any request to understand expectations ahead of a company's earnings date. Always use this skill when the user mentions a ticker in context of upcoming earnings, even if they don't say "preview" explicitly.
earnings-recap
by himself65Generate a post-earnings analysis for any stock using Yahoo Finance data. Use when the user wants to review what happened after earnings, understand beat/miss results, see stock reaction, or get an earnings recap. Triggers: "AAPL earnings recap", "how did TSLA earnings go", "MSFT earnings results", "did NVDA beat earnings", "post-earnings analysis", "earnings surprise", "what happened with GOOGL earnings", "earnings reaction", "stock moved after earnings", "EPS beat or miss", "revenue beat or miss", "quarterly results for", "how were earnings", "AMZN reported last night", "earnings call recap", or any request about a company's recent earnings outcome. Use this skill when the user references a past earnings event, even if they just say "AAPL reported" or "how did they do".
estimate-analysis
by himself65Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data. Use when the user wants to understand analyst estimate direction, how EPS or revenue forecasts changed over time, compare estimate distributions, or analyze growth projections across periods. Triggers: "estimate analysis for AAPL", "analyst estimate trends for NVDA", "EPS revisions for TSLA", "how have estimates changed for MSFT", "estimate revisions", "EPS trend", "revenue estimates", "consensus changes", "analyst estimates", "estimate distribution", "growth estimates for", "estimate momentum", "revision trend", "forward estimates", "next quarter estimates", "annual estimates", "estimate spread", "bull vs bear estimates", "estimate range", or any request about tracking or comparing analyst estimates/revisions. Use this skill when the user asks about estimates beyond a simple lookup — if they want context, trends, or analysis, this is the right skill.
funda-data
by himself65Query Funda AI financial data via two surfaces: the MCP server at https://funda.ai/api/mcp for analyst-grade research synthesis (DCF, comps, earnings previews/recaps, sector deep-dives, SEC filings, transcripts, supply-chain mapping, ownership flow, macro framing) via the agent_chat tool — OR the REST API at https://api.funda.ai/v1 with FUNDA_API_KEY for raw data (real-time quotes, intraday candles, EOD prices, financial statements, options chains/greeks/GEX, supply-chain KG, social sentiment, news, calendars, FRED, ESG, congressional trades, AI hiring signals). Triggers: "funda", "funda.ai", real-time quote, stock price, intraday, balance sheet, income statement, options chain, DCF, comps, earnings preview/recap, analyst estimates, 10-K/10-Q/8-K, transcript, ownership flow, gamma exposure, supply chain, sector deep-dive, congressional trades, FRED. Prefer MCP for synthesis/analysis questions; use REST for raw structured data the MCP declines.
hyperliquid-reader
by himself65Read Hyperliquid (app.hyperliquid.xyz) perp + spot market data via opencli (read-only, public info API). Use whenever the user wants Hyperliquid perpetual or spot markets, mark/oracle/mid prices, 24h change, funding rates (hourly or annualized APR), open interest, volume, the L2 order book, OHLCV candles, historical funding, or a cross-venue funding comparison (Hyperliquid vs Binance vs Bybit) for funding arbitrage. Triggers: "Hyperliquid funding for BTC", "HL perp markets", "funding on BTC perp", "Hyperliquid order book", "HL open interest", "funding arb Hyperliquid vs Binance", "Hyperliquid candles for SOL", "Hyperliquid spot markets", "PURR price on Hyperliquid", "hyperliquid", "hyperliquid.xyz", "HL DEX". READ-ONLY market data — no account, order, or trade operations.
hormuz-strait
by himself65Check the current status of the Strait of Hormuz — shipping transit data, oil price impact, stranded vessels, insurance risk levels, diplomatic developments, and global trade impact. Use this skill whenever the user asks about the Strait of Hormuz, Hormuz chokepoint, Persian Gulf shipping risk, oil transit disruption, war risk premium in the Gulf, Middle East shipping routes, tanker traffic through Hormuz, oil supply chain risk, or geopolitical risk affecting energy markets. Triggers include: "Hormuz status", "Strait of Hormuz", "is Hormuz open", "shipping through the Gulf", "oil chokepoint", "Persian Gulf tanker traffic", "war risk premium", "Hormuz crisis", "energy supply chain risk", "oil transit disruption", "Middle East shipping", any mention of Hormuz or Persian Gulf in context of oil, shipping, or geopolitical risk.
opencli-reader
by himself65Generic read-only fallback for any source opencli covers but this repo has no dedicated reader for — Yahoo Finance, Bloomberg, Reuters, Barchart, Eastmoney, Xueqiu, Sinafinance, Reddit, HackerNews, Substack, Medium, Weibo, Bilibili, Xiaohongshu, Zhihu, arXiv, Google Scholar, Apple Podcasts, Xiaoyuzhou, Spotify, YouTube, Weixin, Amazon, and more. Triggers: "use opencli to read", "grab the frontpage from hackernews", "read reddit r/wallstreetbets", "fetch Eastmoney hot stocks", "pull Xueqiu feed", "get Bloomberg markets headlines", "search arXiv for", any request to read from a site where a specialized skill does not exist but opencli does. FALLBACK — prefer twitter-reader, linkedin-reader, discord-reader, telegram-reader, or yc-reader when the source matches. READ-ONLY — never invoke write operations.
yfinance-data
by himself65Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of stock price, ticker symbol (AAPL, MSFT, TSLA, etc.), "get me the financials", "show earnings", "what's the price of", "download stock data", "options chain", "dividend history", "balance sheet", "income statement", "cash flow", "analyst targets", "institutional holders", "compare stocks", "screen for stocks", or any request involving Yahoo Finance data. Always use this skill even if the user only provides a ticker — infer intent from context.
yc-reader
by himself65Look up Y Combinator companies, batches, and startup ecosystem data using the yc-oss API (read-only). Use this skill whenever the user wants to research YC-backed startups, find companies in a specific batch or industry, check which YC companies are hiring, explore top YC companies, or analyze startup trends by sector or tag. Triggers include: "YC companies in fintech", "who's in the latest YC batch", "YC startups hiring", "top Y Combinator companies", "find YC companies tagged AI", "W25 batch", "S24 companies", "YC stats", "Y Combinator portfolio", "startup research", "which YC companies do X", "venture research on YC", any mention of Y Combinator, YC batch, or YC-backed companies in the context of startup research, venture analysis, or market intelligence. This is a read-only data source — the API is a static JSON dataset updated daily.
linkedin-reader
by himself65Read LinkedIn for financial research using opencli (read-only). Use this skill whenever the user wants to read their LinkedIn feed, search for jobs in the finance/trading industry, view professional posts about markets or earnings, or gather professional sentiment from LinkedIn. Triggers include: "check my LinkedIn feed", "search LinkedIn for", "LinkedIn posts about", "what's on LinkedIn about AAPL", "finance jobs on LinkedIn", "LinkedIn market sentiment", "who's posting about earnings on LinkedIn", "LinkedIn feed", "professional network buzz", "what are analysts saying on LinkedIn", any mention of LinkedIn in context of reading financial news, market research, job searches, or professional commentary. This skill is READ-ONLY — it does NOT support posting, liking, commenting, connecting, or any write operations.
startup-analysis
by himself65Analyze a startup from three perspectives: VC investor, job applicant, and CEO/founder. Use this skill whenever the user wants to evaluate a startup, assess whether to invest in or join a startup, do due diligence, evaluate a job offer from a startup, understand a startup's competitive position, or assess company health and trajectory. Triggers: "analyze this startup", "should I join [company]", "is [company] a good investment", "evaluate [company]", "due diligence on [company]", "what do you think of [startup]", "should I take this startup job offer", "how healthy is [company]", "startup assessment", "company analysis", "is [company] worth joining", "what's the outlook for [company]", "research [company] for me", any mention of evaluating or assessing a startup or tech company from investment, career, or strategic perspectives — provide all three perspectives by default.
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.