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...
liangxi-trader
by 1sh1roUse when the user wants market commentary, setup extraction, trade-plan drafting, or tweet-style interpretation in the style of trader 凉兮, based on the publicly indexed X content currently most closely associated with @WallStreet0Name. Covers his execution habits, preferred structures, risk framing, and blunt Chinese trading voice.
james-wynn-hyperliquid
by 1sh1roUse when the user wants to inspect or discuss James Wynn's public Hyperliquid trading behavior, wallet-level real positions, recent fills, or high-conviction momentum style. Covers best-effort public attribution to wallet 0x5078C2fBeA2b2aD61bc840Bc023E35Fce56BeDb6.
kaito-api
by 1sh1roUse when tasks require Kaito API integration planning for enterprise access, with placeholder request tooling and account-gated endpoint discovery.
alternative-me-api
by 1sh1roUse when tasks need the Alternative.me Fear & Greed Index API (free/no-key) for sentiment sections in daily CEX reports.
altfins-api
by 1sh1roUse when tasks require altFINS API integration planning, key-auth endpoint probes, and premium-access fallback handling.
cex-monthly-secondary-orchestrator
by 1sh1roOrchestrate end-to-end CEX monthly secondary-market reporting by combining Binance/Coinbase/KuCoin benchmark framing with figure skills, Deribit monthly metrics skill, and core report scripts. Includes a v2 polish pass for light-theme charts and finalized monthly-report style.
chaingpt-api
by 1sh1roUse when tasks need ChainGPT API/SDK calls (chat/news/trading assistant) with bearer-token auth and endpoint capability checks.
coingecko-api
by 1sh1roCall the CoinGecko API to fetch crypto prices, market data, coin metadata, or charts; use when a task requires querying CoinGecko endpoints, building a quick data pull, or automating CoinGecko API requests (free or Pro).
coingecko-demo-api
by 1sh1roUse when tasks need CoinGecko data with a Demo/Public key on api.coingecko.com, including endpoint limits, 365-day history constraints, and repeatable pull commands.
coinglass-api
by 1sh1roUse when tasks need Coinglass derivatives data (OI, funding, liquidations) with API-key auth, including paid-tier permission checks and fallback behavior.
deribit-api
by 1sh1roUse when tasks require Deribit JSON-RPC market data calls, including ticker, DVOL, book summaries, and permission-aware request shaping for public endpoints.
hyperliquid-public-trader-orchestrator
by 1sh1roUse when the user wants a ranked sweep of public Hyperliquid traders, such as "今天哪些公开交易员最猛", "帮我扫一圈公开仓位", or "按风格给我排一下值得看的 trader". Covers registry-based scanning, live ranking, and trader cards built from wallet-level data.
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.