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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wx-favorites-report
by zhuyansen微信收藏可视化：从加密的微信本地数据库端到端解密、解析，生成交互式 HTML 可视化报告。 触发词：/wx-favorites-report、微信收藏可视化、收藏报告、收藏分析 支持输入：加密/解密后的 Favorite.db（SQLite）或 CSV/JSON 导出文件
daily-alpha-scanner
by zhuyansenOne-click daily alpha scanner that runs a full 5-step on-chain research pipeline using OKX OnchainOS: (1) Hot Token Discovery with narrative categorization (AI, Meme, DeFi, Infra), (2) Smart Money / KOL / Whale buy-signal tracking with sold-ratio scoring, (3) Meme Coin launchpad scanning (pump.fun, fourmeme) with dev reputation and bundle/sniper detection, (4) Batch security audit (honeypot, mintable, fake LP, wash trading), (5) Consolidated briefing with composite scoring (0-100) and BUY / WATCH / AVOID verdicts. Supports Solana, Base, Ethereum, BSC, Arbitrum. Use when the user wants a daily market scan, alpha discovery, token recommendations, or asks 'what to buy today'. Trigger keywords: daily scan, alpha scanner, today's alpha, what to buy, market scan, daily briefing, 每日扫描, 今日推荐, 扫链, 今天买什么, 市场扫描, 链上日报, 每日研报.
daily-alpha-scanner
by zhuyansenOne-click daily alpha scanner that runs a full 7-step on-chain research pipeline using OKX OnchainOS + social sentiment + historical backtesting: (1) Hot Token Discovery with narrative categorization, (2) Smart Money / KOL / Whale buy-signal tracking, (3) Social Sentiment Scan via crypto news (opennews), Reddit, and Twitter KOL tracking (xreach), (4) Meme Coin launchpad scanning with dev reputation and bundle/sniper detection, (5) Batch security audit (honeypot, mintable, fake LP, wash trading), (6) Historical Signal Validation via DexPaprika OHLCV — backtest what happened after past smart money entries, (7) Consolidated briefing with 7-dimension composite scoring (0-100) and BUY / WATCH / AVOID verdicts. Supports Solana, Base, Ethereum, BSC, Arbitrum. Use when the user wants a daily market scan, alpha discovery, token recommendations, or asks 'what to buy today'. Trigger keywords: daily scan, alpha scanner, today's alpha, what to buy, market scan, daily briefing, 每日扫描, 今日推荐, 扫链, 今天买什么, 市场扫描, 链上日报, 每日研报.
onchain-alpha-radar
by zhuyansenOn-chain alpha discovery and research pipeline. Chains together Token Discovery, Holdings Analysis, Smart Money Tracking, and Research Report generation using on-chain data, Twitter intelligence, deep research methodology, and Excalidraw visualizations. Use when the user wants to: research a token, find alpha, track smart money, analyze on-chain holdings, generate a crypto research report, discover trending tokens, or investigate whale/KOL activity. Trigger keywords: "alpha", "research token", "smart money", "whale tracking", "on-chain research", "链上研究", "Token分析", "持仓分析", "研报", "Smart Money", "聪明钱", "巨鲸追踪", "meme coin analysis", "token discovery"
smart-money-sentinel
by zhuyansenSmart money signal tracking + sentiment alert pipeline. Starts from Binance smart money signals, then cross-validates with crypto news, KOL discussions, and wallet position changes to determine optimal entry timing. Chains together: Binance Skills (trading-signal, query-token-info, query-address-info), opennews-mcp (crypto news), opentwitter-mcp (KOL tracking), base-mcp (Base chain wallet), and excalidraw-diagram (visual diagrams). Use when the user wants to: track smart money movements, check entry timing, monitor whale alerts, cross-validate trading signals with news and social sentiment, or get a comprehensive signal report. Trigger keywords: "聪明钱", "smart money signal", "异动", "舆情预警", "信号追踪", "entry timing", "钱包追踪", "whale alert", "信号验证", "入场时机", "聪明钱哨兵", "sentinel", "trading signal", "smart money tracking", "KOL舆情", "新闻验证"
lark-workflow-meeting-automation
by zhuyansen会议自动化：从纪要到待办。会议结束后一键完成：搜索会议 → 获取妙记纪要 → 提取待办 → 创建飞书任务并指派 → 生成纪要文档 → 发送群聊通知。当用户需要自动整理会议纪要、批量创建会议待办任务、发送会议总结通知时使用。
agentskillshub
by zhuyansenUse when the user wants to find, evaluate, audit, or install an open-source AI agent skill or MCP server — e.g. "find an MCP server for Postgres", "is this skill safe to install", "what should I use to scrape a website". Searches a quality-scored, security-graded catalog of ~20K skills locally (cached index, zero backend load) and returns each result's security grade, quality score, and install commands so you can check trust BEFORE installing.
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.