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...
polyclaw
by chainstacklabsTrade on Polymarket via split + CLOB execution. Browse markets, track positions with P&L, discover hedges via LLM. Polygon/Web3.
entering-an-alphapoly-position
by chainstacklabsExecutes a covered pair trade (target + cover) on Polymarket with estimate, confirmation, and position recording. Use when entering a new position from a detected portfolio opportunity. Also use when the user says "buy", "trade", "open a position", "place an order", "enter", or wants to act on any portfolio opportunity.
exiting-an-alphapoly-position
by chainstacklabsSells or merges tokens from an open alphapoly position via CLOB or on-chain merge. Use when exiting, cleaning up, or managing an open position. Also use when the user says "sell", "close position", "cash out", "redeem", "exit", "clear tokens", or wants to get out of a trade.
creating-alphapoly-experiments
by chainstacklabsScaffolds a standalone experiment script in experiments/ using shared backend dependencies. Use when creating a new experiment or prototype outside the main backend. Also use when the user says "prototype", "try something", "test an idea", "spike", "scratch script", "quick experiment", or wants to explore something without modifying the main codebase.
adding-alphapoly-features
by chainstacklabsGuides feature development in the alphapoly codebase following stack conventions (uv, polars, FastAPI+Next.js). Use whenever adding, building, or modifying any feature — including new API endpoints, pipeline steps, UI pages, WebSocket services, or any other backend/frontend work. If the user says "add X", "build X", "implement X", "make X work", "create an endpoint", "wire up", "hook up", or "connect X to Y", use this skill. For quick prototypes, consider alphapoly-experiment instead.
browsing-alphapoly-portfolios
by chainstacklabsFetches and displays current hedging portfolio opportunities from the backend API. Use when browsing or listing portfolio opportunities to trade. Also use when the user says "show me opportunities", "what's available", "what can I trade", "show pairs", "list portfolios", "any good trades", or asks about current market opportunities.
running-the-alphapoly-pipeline
by chainstacklabsRuns, debugs, and manages the alphapoly ML pipeline with make commands and model overrides. Use when running, resetting, or troubleshooting the pipeline. Also use when the user says "refresh data", "update portfolios", "fetch markets", "reprocess", "run the pipeline", or wants to regenerate portfolio opportunities from scratch.
run-mainnet-e2e-tests
by chainstacklabsRun end-to-end tests for pumpfun-cli against Solana mainnet. Use this skill when the user asks for "e2e tests", "end to end tests", "test on mainnet", "QA the CLI", "test with real transactions", or "smoke test against mainnet". WARNING - this skill executes real transactions that cost real SOL. Only use when the user explicitly wants mainnet testing. For safe/free tests use the run-unit-and-surfpool-tests skill instead.
run-unit-and-surfpool-tests
by chainstacklabsRun pumpfun-cli test suites — unit tests and surfpool integration tests. Use this skill whenever the user says "run tests", "run unit tests", "run surfpool tests", "check if tests pass", "test the code", or after making code changes that should be verified. Also trigger when the user asks to validate changes, check for regressions, or verify something works. This skill covers ALL non-mainnet testing. For mainnet e2e testing with real funds, use the run-mainnet-e2e-tests skill instead.
work-item
by chainstacklabsWork item pipeline for pumpfun-cli — investigate, plan, implement (TDD), verify, finalize as PR. MUST use this skill whenever the user wants to start, tackle, pick up, implement, or work on a work item. Triggers on any mention of work items by number ("item 5", "item
pumpclaw
by chainstacklabsExecute pump.fun token trades via pumpfun-cli. Handles buy/sell with smart routing (bonding curve vs PumpSwap AMM), slippage control, priority fees, compute units, and dry-run simulation. Also covers token launch, migration, wallet management, and token discovery. Use when the user wants to trade, swap, buy, sell, launch, or manage pump.fun tokens.
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.