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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ai-trader-copytrade
by HKUDSFollow top traders and automatically copy their positions.
ai-trader-tradesync
by HKUDSSync your trading positions and trade records to AI-Trader copy trading platform.
portfolio-distribution
by Drakkar-SoftwareUse this skill for making final portfolio allocation decisions, determining optimal position sizing, and providing actionable trading recommendations based on risk assessments.
recap
by davepoonTriggered by "monthly recap", "how did I do this month", "spending summary", "financial review", "weekly recap", "quarterly review", "year in review"
closing-costs
by davepoonCalculates itemized state-specific closing costs for mortgage refinance transactions across 10 licensed states, with product-specific fees for Conventional, FHA, FHA Streamline, VA IRRRL, and VA Cash-Out.
analyze-pitch-deck
by davepoonActivate for ANY pitch deck analysis, feedback, or review request. Triggers include: "analyze this deck", "review my pitch deck", "critique my pitch", "feedback on my slides", "is my deck investor ready", "what's wrong with my pitch", "how would a VC react to this deck", "score my pitch deck", "rate my slides", "improve my deck", "what slides am I missing", "is this pitch compelling". Also triggers when a user pastes slide content, describes their deck structure, or shares a company narrative and asks for investor feedback. Works on claude.ai and Claude Code.
deal-sourcing-signals
by davepoonScan a company or sector for deal-sourcing signals across 6 dimensions. Triggered by: "/venture-capital-intelligence:deal-sourcing-signals", "scan signals for X", "what signals is X showing", "deal sourcing scan", "hiring signals for X", "is X raising soon", "monitor this company", "company signal scan", "sourcing brief for X", "what is X up to", "is X growing", "track this company", "deal signal report for X", "is this company fundraising", "what are the momentum signals for X", "find signals on X", "is X worth tracking". Claude Code only. Requires Python 3.x. Uses web search for live signal data.
soft-screening-startup
by davepoonActivate for ANY startup evaluation, investment screening, or company assessment. Triggers include: "evaluate this startup", "screen this company", "should I invest in X", "is this a good investment", "what do you think about this company", "review this startup", "score this company", "rate this pitch", "assess this founder", "quick take on X", "is X worth investing in", "pass or decline on X", "what's your verdict on X", "first look at this company", "quick screen on X", "what's your take on this founder", "is this fundable", "would a VC invest in this". Also triggers when a user pastes a company description, funding ask, or founder background and asks for an opinion. Works on claude.ai and Claude Code. For hard-mode deterministic scoring with Python audit trail, use /venture-capital-intelligence:hard-screening-startup.
shop
by LeoYeAIClaw goes shopping. Give your claw a creditcard. Financial management for Agents and OpenClaw bots.
portfolio-manager
by tradermontyComprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.
compound-interest-simulator
by revfactoryand simulation and basis asset nature example tool. 'investment-advisor' and 'tax-strategist' agent investment revenue simulationand retirement specialist designto do when this skill's official, scenario comparison, retirement goal total must be utilized. ' total', 'asset nature example', 'retirement specialist simulation' etc. However, budget design tax total is outside this skill's scope.
financial-ratio-analyzer
by revfactoryitemsperson financial casebeforenature diagnosis financial ratio analysis tool. 'financial-analyst' and 'finance-reviewer' agent financial status diagnosisand figure verifyto do when this skill's financial ratio official, casebeforenature standard, diagnosis framework must be utilized. 'financial casebeforenature diagnosis', 'financial ratio analysis', 'total financial inspection' etc. However, investment strategy tax savings approach is outside this skill's scope.
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.