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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codex-app-parity
by friuns2Use only when the user explicitly mentions Codex parity, codex-app-parity, Codex.app parity, or asks to compare against the installed Codex desktop app.
github-pr-acceptance
by friuns2Use when the user explicitly asks to accept or merge a GitHub pull request and the PR must show as merged on GitHub, not only in local git history.
github-pr-review-comments
by friuns2Use when the user asks to review GitHub pull requests, identify concrete risks, and write review comments with `gh`. Focus on actionable findings, not summaries.
upstream-sync-curator
by friuns2用于这个仓库中“先分析 upstream 提交与代码变更，再按功能主题选择性引入 upstream 变更；所有操作在临时 worktree 中完成，并在难解冲突时优先保留当前 fork 实现”。当用户提到 upstream sync、同步源仓库、选择性合并 upstream、分析上游提交、挑功能合并等意图时使用。仅适用于 nervmor/codexui 与其上游 friuns2/codexUI。
launch-codex-unpacked
by friuns2Launch unpacked Codex Desktop builds with debug ports and optional SSH host autostart using launch_codex_unpacked.sh. Use when asked to run Codex from extracted app.asar, enable inspect/remote-debugging, select an SSH host on startup, or keep temporary unpacked artifacts for investigation.
notion-cli-mcp
by friuns2Notion via notion-cli — a Rust CLI + MCP server for Notion API 2025-09-03+. Three-tier agent integration (read-only default, opt-in runtime writes, opt-in admin lifecycle) with rate limiting, response-size cap, untrusted-source output envelope, per-tier JSONL audit logs, and --check-request dry-runs. Supports the new data-source model, 22 property types, 12 block types, admin schema mutation, relation wiring, dedicated page-move endpoint, db update, and users me (v0.4).
obsidian-helper
by friuns2Operate Obsidian vaults from command line. Use when the user wants to list, search, create, read, edit, or delete Obsidian notes, or manage daily notes. Triggers on mentions of "obsidian笔记", "obsidian notes", "obsidian搜索", "obsidian创建", "obsidian列表", or any Obsidian vault operations.
polymarket-nothing-ever-happens
by friuns2Buy NO on standalone non-sports yes/no Polymarket markets priced below a configurable cap. Based on the "nothing-ever-happens" thesis — binary markets often resolve NO, and cheap NO shares offer asymmetric value. Scans for candidates via Gamma API, filters out sports and grouped markets, checks fees, and executes.
icecube-content-factory
by friuns2🧊 IceCube Content Factory — Turn any topic into viral-worthy content. Auto-generate hooks, threads, and posts with engagement psychology built-in. When users mention 'content creation', 'viral posts', 'social media content', 'write threads', 'content hooks', 'engagement optimization'.
social-media-scholar
by friuns2把论文帖分享给我，我帮你加进zotero：从公众号/小红书/X等社交媒体文章链接提取论文信息，将论文保存到 Zotero 文库。(When you share a link of social media article that contains citation a paper, this skill can extract the paper information and save it to Zotero library.)
kr-crypto-intelligence
by friuns2Korean crypto market data + AI analysis for trading agents. 13 tools — Kimchi Premium across 180+ tokens, exchange intelligence, AI sentiment analysis (world's first Korean-to-English), Global vs Korea divergence with structured AI breakdown, market alerts. x402 on Base, Polygon, and Solana.
beautyplus-ai
by friuns2BeautyPlus portrait beautification: body reshape (breast/butt presets with strength tiers), hair color and hairstyle, outfit change (formal / vacation / cosplay / party / sports), face style, expression (smile / wink / cool), and photo art (tan / flash / film).
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.