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...
aicoin-trading
by aicoincom**CEX 中心化交易所**(Binance / OKX / Bybit / Bitget 等)的下单交易工具。严格规则:(1) 所有订单必须通过 node scripts/exchange.mjs create_order 执行,禁止写自定义代码下单 (2) create_order 分两步:第一次返回预览,展示给用户等确认,用户说确认后第二次加 confirmed=true 执行 (3) 禁止自动确认,禁止跳过预览 (4) 平仓必须用 close_position,禁止用 create_order 构建平仓单。Trigger 关键词: 'buy on okx', 'sell on binance', '在 OKX 买 BTC', '在 Binance 下单', '做多 BTC 永续', '杠杆做空 ETH', '平掉我的 SOL 仓位', 'CEX 下单', '现货买入', '合约开仓', '永续平仓', '止盈止损', 'long', 'short', 'leverage', '买', '卖', '下单', '做多', '做空', '开仓', '平仓', '平掉', '关仓'. **路由提示**: 用户说“链上 swap / Uniswap / DEX 买 PEPE / Solana 上买”是**链上 DEX 交易**,应走 `aicoin-onchain` 而不是本 skill. Hyperliquid 上的下单也走 aicoin-onchain(HL 是链上 perp DEX),不是这里. 本 skill **只**处理 CEX 现货 + 永续合约下单。
aicoin-hyperliquid
by aicoincomHyperliquid on-chain perpetuals analytics from AiCoin Open API v3 — the primary source for on-chain whale / smart-money / large-fund movement. Use this skill when the user asks about: Hyperliquid whale positions, HL liquidations, HL open interest, HL trader analytics, HL taker flow, HL funding history, AND generic on-chain whale activity — '链上大资金动向', '链上鲸鱼', '聪明钱', '大户在干嘛', 'on-chain whale', 'smart money', 'Hyperliquid大户', 'HL鲸鱼', 'HL持仓', 'HL清算', 'HL持仓量', 'HL交易员', 'HL 资金费率' — because HL is the deepest on-chain perp venue and AiCoin exposes its whale positions / events / liquidations / trader stats without needing any wallet key. For general crypto prices/news use aicoin-market; for DEX swaps / wallets use aicoin-onchain; for CEX trading use aicoin-trading; for Freqtrade use aicoin-freqtrade.
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.