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...
literature-evidence-matcher
by adoreseverMatch manuscript claims against user-provided literature and produce a traceable claim-evidence table. Use when the user asks to add references, check whether PDFs support manuscript statements, find supporting or opposing evidence from a literature folder, distinguish strong support from background support, or create a 论点-证据匹配 report for an academic draft.
paper-workbench
by adoreseverBuild or reorganize an academic paper project workspace with clear folders and local instructions for literature, drafts, feedback, extracted text, and output reports. Use when the user wants to set up a论文工作台, organize mixed manuscript materials, create project rules, or prepare a stable workflow before citation checking, evidence matching, reviewer simulation, or manuscript revision.
reviewer-simulator
by adoreseverSimulate a strict academic reviewer and produce a manuscript risk report without editing the draft. Use when the user asks to act as a reviewer, find weaknesses before submission, inspect evidence gaps, check overclaims, evaluate manuscript readiness, combine advisor notes or meeting minutes into revision priorities, or generate a 审稿人模拟检查 report.
web-scraping
by adoreseverWeb scraping tools for fetching and extracting data from web pages
auto-export-monitor
by adoresever汽车出口政策新闻监控：自动爬取商务部、海关总署、中国信保、汽车流通协会等权威网站的最新政策和行业动态，AI分析生成风险研判和决策建议。Use when: (1) 用户询问汽车出口政策/关税/法规变化, (2) 需要获取最新汽车行业新闻, (3) 定时推送政策情报简报, (4) 查询某国出口风险评级。触发词: 汽车出口、政策监控、风险预警、关税变化、出口信保、行业动态。
youtube-clipper
by adoreseverYouTube 视频智能剪辑工具。下载视频和字幕，AI 分析生成精细章节（几分钟级别）， 用户选择片段后自动剪辑、翻译字幕为中英双语、烧录字幕到视频，并生成总结文案。 使用场景：当用户需要剪辑 YouTube 视频、生成短视频片段、制作双语字幕版本时。 关键词：视频剪辑、YouTube、字幕翻译、双语字幕、视频下载、clip video
reviewer-simulator
by adoreseverSimulate a strict academic reviewer and produce a manuscript risk report without editing the draft. Use when the user asks to act as a reviewer, find weaknesses before submission, inspect evidence gaps, check overclaims, evaluate manuscript readiness, combine advisor notes or meeting minutes into revision priorities, or generate a 审稿人模拟检查 report.
opennews
by adoreseverCrypto news search, AI ratings, trading signals, and real-time updates via the OpenNews 6551 API. Supports keyword search, coin filtering, source filtering, AI score ranking, and WebSocket live feeds.
backup
by adoreseverBackup and restore openclaw configuration, skills, commands, and settings. Sync across devices, version control with git, automate backups, and migrate to new machines.
task-status
by adoreseverSend short status descriptions in chat for long-running tasks. Use when you need to provide periodic updates during multi-step operations, confirm task completion, or notify of failures. Includes automated periodic monitoring that sends updates every 5 seconds, status message templates, and a helper function for consistent status reporting.
literature-evidence-matcher
by adoreseverMatch manuscript claims against user-provided literature and produce a traceable claim-evidence table. Use when the user asks to add references, check whether PDFs support manuscript statements, find supporting or opposing evidence from a literature folder, distinguish strong support from background support, or create a 论点-证据匹配 report for an academic draft.
qwen-image
by adoresever通义千问AI生成图片并发送钉钉。触发词：画图、生成图片、画一个、画一张、创作图片。禁止使用SVG，必须执行脚本。
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.