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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claude-statusbar
by leeguoooooManage `cs` (claude-statusbar) — switch theme/style/density, override severity colors, preview combinations, run doctor, reset config, install or remove the bar, toggle fast/daemon mode, show cost or prompt-cache age, or toggle the activity segments (todos, active tool, running subagents, session duration, lines changed, git ahead/behind). Use whenever the user mentions cs, claude-statusbar, status bar, status line, 状态栏, 主题, theme switching, style switching, color customization, 余量颜色, 警告颜色, severity color, /statusbar, cs preview, cs doctor, fast mode, daemon, refreshInterval, 5h/7d window, context window display, prompt cache, todos / 待办, active tool, subagents / 子agent, session duration / 时长, lines changed / 行数, git ahead-behind / 领先落后, forecast / 预测 / 还能用多久, at-risk chip, show_forecast, or asks to install / configure / diagnose / customize the bottom status line in Claude Code.
claude-statusbar
by leeguoooooManage `cs` (claude-statusbar) — switch theme/style/density, override severity colors, preview combinations, run doctor, reset config, install or remove the bar, toggle fast/daemon mode, show cost or prompt-cache age. Use whenever the user mentions cs, claude-statusbar, status bar, status line, 状态栏, 主题, theme switching, style switching, color customization, 余量颜色, 警告颜色, severity color, /statusbar, cs preview, cs doctor, fast mode, daemon, refreshInterval, 5h/7d window, context window display, prompt cache, or asks to install / configure / diagnose / customize the bottom status line in Claude Code.
chatgpt-imagegen
by leeguoooooGenerate raster images (PNG/JPEG/WebP) using the user's ChatGPT subscription via a local one-file Python CLI — no OPENAI_API_KEY, no gateway, no daemon. Two backends: web (default) drives the user's logged-in ChatGPT browser so generation runs on the conversation surface and does NOT consume Codex-usage limits; codex is a headless fallback that bills the Codex-usage bucket. Use when an agent needs to create a brand-new bitmap asset for the current project (photos, illustrations, icons, hero banners, mockups, sprites, concept art) and the output should be a bitmap file saved into the workspace. Do not use when the task is better solved by editing existing SVG/vector assets, writing code-native graphics (HTML/CSS/canvas), or extending an established repo icon system.
yapi
by leeguoooooQuery and sync YApi interface documentation. Use when user mentions "yapi 接口文档", YAPI docs, asks for request/response details, or needs docs sync. Also triggers when user pastes a YApi URL that matches the configured base_url.
yapi
by leeguoooooQuery and sync YApi interface documentation. Use when user mentions "yapi 接口文档", YAPI docs, asks for request/response details, or needs docs sync. Also triggers when user pastes a YApi URL that matches the configured base_url.
codex-api-imagegen
by leeguoooooGenerate raster images (PNG/JPEG/WebP) via the local codex-api gateway, powered by the user's ChatGPT subscription — no OPENAI_API_KEY needed. Use when an agent needs to create a brand-new bitmap asset for the current project (photos, illustrations, icons, hero banners, mockups, sprites, concept art) and the output should be a bitmap file saved into the workspace. Do not use when the task is better solved by editing existing SVG/vector assets, writing code-native graphics (HTML/CSS/canvas), or extending an established repo icon system.
macOS WeChat CLI + local HTTP bridge + Wechaty Puppet gRPC gateway — send messages, query sessions / contacts / chat history / images / favorites, and expose stable HTTP / gRPC surfaces for agent integration. Use when the user asks to 'send a WeChat message', '发微信', query WeChat contacts/groups/messages, look up who said what in a chat, fetch images from history, export chat history, wire WeChat into Hermes / n8n / Dify / LangChain, or run any wechaty bot on a real macOS WeChat account. Requires WeChat 4.1.8 / 4.1.9 on macOS (Apple Silicon) and a `wxp_act_` activation code. One-time `wechat init` extracts the DB key; no sudo, no re-signing WeChat.app. Optional remote bridge — `wechat tunnel setup --hostname <yours>` exposes the local REST API via Cloudflare Tunnel for remote services to call.
mailbox
by leeguoooooRead, search, send, and manage email across Gmail, QQ, 163, Outlook and any IMAP/SMTP account from the command line. Use when the user asks to "read my email", "查邮件", "look up an Amazon order email", "find the customer review notification", "send an email", "回复邮件", "delete spam", "查未读", "show unread", "synchronize my mailbox", "set up MCP for email", or anything that involves listing / searching / reading / writing / classifying messages from one or more mailboxes.
zentao
by leeguoooooDEPRECATED — prefer the official zentao-cli (https://github.com/easysoft/zentao-cli) and its skills (https://github.com/easysoft/zentao-skills) for new setups. This skill still works for existing @leeguoo/zentao-mcp users and remains useful for its bug resolution stats command. Use the zentao CLI to query and operate ZenTao bugs, tasks, stories, todos, products, programs, projects, executions, plans, releases, test cases, test tasks, test suites, docs, users, departments, issues, and risks. Use when the user mentions 禅道 or ZenTao, wants bug/task/story/todo/project/test/doc lookups or updates, wants bug resolution rate stats or reports (bug 解决率统计/解决数量统计/bug statistics/bug report) by product or person, or needs login / whoami / self-test for a 禅道 instance. ZENTAO_URL usually includes /zentao.
xhs-skill
by leeguooooo小红书（创作者中心）登录拿 cookies、发布笔记、导出数据的单一入口技能（浏览器交互委托 agent-browser-stealth）
wrangler-accounts
by leeguoooooAWS-style multi-account convenience for Cloudflare Wrangler. Use when you need to run wrangler commands against a specific Cloudflare account, manage saved OAuth profiles, set or switch the persistent default profile, or open an isolated subshell for a profile. Prefer --json for machine-readable output.
paypay-securities
by leeguoooooCLI & agent skill for a PayPay証券 (PayPay Securities, ペイペイ証券) account: check portfolio, holdings, balance, 投資信託, 米国株, 取引履歴 (transaction history), fees & FX-spread; generate a 復盘/review with realized & unrealized P&L and money-weighted return (XIRR); 持仓结构/exposure (risk), 定投/つみたて recurring-buy plans (plans), per-year tax view (tax), account snapshots & diff (snapshot/diff), multi-account consolidation (-a all), Chinese output (--lang zh); AND place 米国株 orders (buy/sell/cancel). Use when the user wants to view, review, analyze, report, or trade a PayPay証券 / PayPay investment account. Orders dry-run by default; a live order requires an explicit human `--execute` with a typed confirmation + the account TRADE_PASSWORD (the agent never auto-submits). Facts only — never investment advice or buy/sell judgments.
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.