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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unifai
by unifai-networkA CLI for searching and invoking services on the UnifAI network. Supports 40+ services across DeFi, token data, social media, web search, news, travel, sports, and utilities.
social-card-composer
by unifai-networkA universal social media card and layout generator with automatic AI background removal. Use this when a user wants to remove backgrounds from subjects (images) and composite them into customizable, aesthetic posters for social media (e.g., comparison cards, lookbooks, product showcases). Supports fully customizable titles, subtitles, background images, and text boxes. Now natively supports twin engines satori (default for text-heavy layouts) and pillow (for shadow-heavy 1:1 image comparisons).
trend-to-redbook-automation
by unifai-networkEnd-to-end automation orchestrator. Fetches data from Twitter (X) or global News MCPs, passes it through the detoxifier for compliance, formats it via the content-creator, and renders/publishes to Redbook using the local render engine.
unifai
by unifai-networkUnifAI CLI for searching and invoking services across DeFi, token data, social media, web search, news, travel, sports, and more.
agent-bench
by unifai-networkBenchmark and compare the agentic performance of multiple LLM models on the same task. Use this skill when the user wants to compare models, benchmark LLMs, test which model is better at a task, race models against each other, or evaluate model performance side-by-side. Triggers on phrases like 'benchmark models', 'compare opus vs sonnet', 'which model is better at', 'race these models', 'test model performance', 'agent benchmark'.
crawlee-data-extractor-skills
by unifai-networkEnterprise-grade web scraping and data extraction skill powered by Crawlee. Features anti-blocking stealth browsers, proxy rotation, and structured JSON output for dynamic web apps.
matrix-workflow
by unifai-network全自动矩阵号管家，支持"囤素材模式（生成打包）"与"全自动分发模式（生成过审发帖）"，针对中国社交平台（如小红书）内置防风控、合规洗稿与网感图文渲染流。当用户要求做图文、视频或发布自媒体笔记时触发。
redbook-anti-risk-detoxifier-skills
by unifai-networkCopywriting detoxification & compliance formatting skill. An essential compliance filter invoked prior to publishing content on domestic social media platforms (such as Redbook, TikTok, WeChat) to defend against image OCR surveillance and banned word detection. Crucial for neutralizing sensitive terms, hardcore tech jargon, and scraping rhetoric.
redbook-content-creator-skills
by unifai-networkGenerate Redbook (Xiaohongshu) content optimized for the platform's CES algorithm. Use when planning content calendars, writing social media text, or optimizing SEO. Supports diary-style, tutorial, review, and list formats.
redbook-render-skills
by unifai-networkRedbook (Xiaohongshu) material rendering and publishing skill. Used to convert markdown text into stunning, natively formatted image cards (cover + content pages) and optionally publish them automatically to the platform. Supports 8 gorgeous CSS themes and 4 smart pagination modes.
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.