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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vscode-httpyac-config
by libukaiConfigure VSCode with httpYac for API testing and automation. This skill should be used specifically when converting API documentation to executable .http files (10+ endpoints), setting up authentication flows with pre-request scripts, implementing request chaining with response data, organizing multi-file collections with environment management, or establishing Git-based API testing workflows with CI/CD integration.
vscode-port-monitor-config
by libukaiThis skill should be used when configuring VS Code Port Monitor extension for development server monitoring. Use when the user asks to "set up port monitoring for Vite", "monitor my dev server ports", "configure port monitor for Next.js", "track which ports are running", "set up multi-port monitoring", "monitor frontend and backend ports", or "check port status in VS Code". Provides ready-to-use configuration templates for Vite (5173), Next.js (3000), and microservices architectures with troubleshooting guidance.
vscode-sftp-config
by libukaiThis skill should be used when setting up SFTP deployment for static websites to production servers, including converting projects from Docker/Express to static hosting, deploying Vue/React/Angular builds, setting up Slidev presentations, or configuring Hugo/Jekyll/Gatsby sites. Use this when the user asks to "setup SFTP deployment", "deploy static site to server", "configure Nginx for static files", "convert from Docker to static hosting", "deploy Vue build to production", "setup subdomain hosting", or "configure SFTP in VS Code". Provides SFTP configuration templates and production-ready Nginx configurations with security headers and caching.
obsidian-to-x
by libukai发布内容和文章到 X (Twitter)。支持常规推文(文字/图片/视频)和 X Articles(长文 Markdown)。使用真实 Chrome 浏览器绕过反机器人检测。当用户说"发推"、"发到 X"、"发到 twitter"、"分享到 X"、"分享到 twitter"、"发 tweet"、"同步到 X"、"发布到 X"、提到"X Articles"、想从 Obsidian 笔记发布长文内容、或需要转换 Obsidian Markdown 到 X 格式时使用。适用于所有 X/Twitter 发布任务。
mcp-config
by libukaiConfigure MCP (Model Context Protocol) servers for Claude Code. Manage MCP servers at user or project scope with best practices to avoid context pollution.
plugin-integration-checker
by libukaiCheck if a skill is part of a plugin and verify its integration with commands and agents. Use after creating or modifying a skill to ensure proper plugin architecture. Triggers on "check plugin integration", "verify skill integration", "is this skill in a plugin", "check command-skill-agent integration", or after skill creation/modification when the skill path contains ".claude-plugins" or "plugins/".
skill-creator-pro
by libukaiCreate new skills, modify and improve existing skills, and measure skill performance. Enhanced version with quick commands. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Triggers on phrases like "make a skill", "create a new skill", "build a skill for", "improve this skill", "optimize my skill", "test my skill", "turn this into a skill", "skill description optimization", or "help me create a skill".
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.