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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aphorisms
by danielmiesslerManages a curated aphorism collection with full CRUD — content-based matching, themed search, thinker research, and database maintenance. Organizes quotes by author, theme, context, and newsletter usage history to prevent repetition. Four workflows: FindAphorism (analyze newsletter content, match themes, return 3-5 ranked recommendations with rationale), AddAphorism (parse quote + author, extract themes, validate uniqueness, update theme index), ResearchThinker (deep research on philosopher, add sourced quotes to database), SearchAphorisms (search by theme, keyword, or author). Database at ~/.claude/skills/aphorisms/Database/aphorisms.md — stores full quote text, author attribution, theme tags, context/background, source reference, and usage history per entry. Theme index supports 12+ categories: Work Ethic, Resilience, Learning, Stoicism, Risk, Wisdom, Truth-seeking, Excellence, Curiosity, Freedom, Rationality, Clarity. Supported thinkers: Hitchens, Feynman, Deutsch, Sam Harris, Spinoza, plus any requested a
script-rewriter
by chatfire-AI小说改写为格式化剧本的方法论和规范
axiom-explainer
by digoalWrite Chinese Markdown articles for university students and adults with social experience that explain one viewpoint, axiom, theorem, law, principle, or theory system through "求真讲法、求存讲法、思考", with text/Mermaid and standalone GitHub-renderable SVG files, assumptions, derivation or motivation, applicability boundaries, positive and negative examples, and save the result under the current project's markdown directory. Use when Codex is asked to generate 公理讲解文案、定理讲解、理论体系通俗解读、观点拆解、面向大学生和成年人的图文并茂 Markdown 文章.
article-rewriter
by digoal给定一篇文章的 URL 或全文内容，深度阅读消化后，以更高屋建瓴的视角创作一篇全新文章： 提取核心观点+基石假设，找逻辑漏洞或用第一性原理重构假设，续写新内容，最终输出图文并茂的 Markdown 文章。 触发条件：用户提供文章链接或粘贴文章内容，并说"帮我改写"、"写一篇更高层次的文章"、"找逻辑漏洞"、 "重构观点"、"消化后重写"、"升华这篇文章"、"基于这篇文章写一篇新的"，或用户说"这篇文章有什么问题/ 漏洞/不足"并希望输出新文章时，必须使用本 skill。即使用户只说"帮我把这篇文章写得更好"或"这篇文章太 浅了，帮我深化一下"，也应使用本 skill。
podcast-script
by digoal将 Markdown 文章转换成 N 人播客脚本（1～4人），输出为 .txt 文件保存至当前项目的 markdown/ 目录。触发条件：用户提到"转成播客"、"生成播客脚本"、"把这篇文章做成播客"、"播客对话"、"podcast script"、"N个人的播客"，或者上传了一个 markdown 文件并希望以播客形式呈现。即使用户只说"帮我把这篇文章转成播客"，也应使用本 skill。默认输出中文脚本，除非用户明确指定其他语言。
article-to-podcast-script
by digoalConvert a Markdown article into a natural first-person podcast script for 1 to 4 speakers and save it as a .txt file under the current project's markdown directory. Default to Chinese output unless the user explicitly specifies another language, while preserving English terms that appear in the article. Use when the user provides an article Markdown file and a podcast speaker count, asks for a VibeVoice-style podcast script, dialogue rewrite, narrated audio script, multi-host conversation, solo podcast monologue, or personal-viewpoint audio script based on an article.
dbs-ai-check
by dontbesilent2025dontbesilent AI 写作特征识别。扫描文案中的 AI 生成痕迹，输出检测报告。默认只诊断不改。 触发方式：/dbs-ai-check、/AI检测、「帮我看看有没有 AI 味」「检测一下 AI 特征」 AI writing fingerprint detection. Scans copy for AI-generated patterns and outputs a diagnostic report. Diagnosis only by default. Trigger: /dbs-ai-check, "check for AI writing", "does this sound like AI"
dbs-hook
by dontbesilent2025dontbesilent 短视频开头优化。诊断开头问题 + 生成优化方案。 触发方式：/dbs-hook、/hook、「帮我优化开头」「开头怎么写」 Short video opening optimization with diagnosis and solutions. Trigger: /dbs-hook, "optimize my opening", "how to write opening"
pirate
by genkit-aiRespond in the voice of a swashbuckling pirate.
shakespeare
by genkit-aiRespond in the style of William Shakespeare — early modern English, poetic cadence.
article-writer
by netease-youdaoMulti-style article creation skill. Supports 5 writing styles (deep analysis, practical guide, story-driven, opinion, news brief), including complete workflow: material collection → outline → content → formatting. Activated when users mention "write article", "write post", "create", or "draft".
source-command-audit-prose
by FlorianBruniauxAdd descriptive prose to bare sections in whitepapers (FR + EN) using 9 parallel agents
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.