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...
hana-plugin-creator
by liliMoziCreate Hana plugin scaffolds and guide users through beginner or developer plugin planning, capability checks, manifest setup, runtime tools, iframe UI, Session/Agent APIs, model/media APIs, SDK templates, and install-ready plugin directories. Use when HanaAgent/Codex needs to explain what Hana plugins can do, help a user describe a plugin idea, check whether the SDK supports it, or generate/update a Hana plugin with @hana/plugin-runtime, @hana/plugin-sdk, and @hana/plugin-components.
image-gen-guide
by liliMozi使用图片/视频生成工具时必读。包含工具参数、非阻塞工作流、任务路由。
user-guide
by liliMoziHanaAgent 用户说明书。解答用户关于软件使用方法、功能介绍、常见问题和实用技巧的疑问。 MANDATORY TRIGGERS: 怎么用, 使用方法, 说明书, 帮助文档, 新手指南, 怎么设置, 功能介绍, how to use, user guide, help, getting started, 教程, tutorial, 这是什么功能, 怎么操作
quiet-musing
by liliMoziDeep reasoning framework for complex tasks. Activates for multi-step problems, high uncertainty, or trade-off decisions. 复杂问题推理框架。遇到多步骤、高不确定性、需要权衡取舍的任务时启用。Triggers: analyze complex problem, make decision, weigh options, debug hard bug, architecture design, strategy planning, think it through, help me analyze, this is complicated, deep thinking | 触发场景：分析复杂问题、做决策、权衡方案、调试疑难 bug、架构设计、策略规划、想清楚再做、帮我分析一下、这个问题比较复杂、深度思考。Do NOT activate for simple Q&A, casual chat, or single-step tasks. 不要在简单问答、闲聊、单步操作时启用。
skill-creator
by liliMoziCreate new skills, modify and improve existing skills, and measure skill performance. 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. 创建新技能、修改和改进现有技能、衡量技能表现。当用户想要从零创建技能、更新或优化现有技能、运行评估测试技能、通过方差分析进行性能基准测试，或优化技能描述以提高触发准确率时使用。 MANDATORY TRIGGERS: create skill, new skill, improve skill, skill eval, benchmark skill, 创建技能, 新技能, 改进技能, 评估技能
office-documents
by liliMoziUse when the user asks to open, read, inspect, understand, summarize, analyze, extract tables/text from, modify, update, repair, split, merge, rotate, or convert information from PDF, DOCX, XLSX, XLSM, or PPTX files, including when they mention Word, Excel, PowerPoint, spreadsheet, presentation, Office document, or PDF in passing. 读取或修改 PDF、Word、Excel、PPT 文件时必须使用。
hyperframes-cli
by liliMoziHyperFrames CLI dev loop — `npx hyperframes` for scaffolding (init), validation (lint, inspect), preview, render, and environment troubleshooting (doctor, browser, info, upgrade). Use when running any of these commands or troubleshooting the HyperFrames build/render environment. For asset preprocessing commands (`tts`, `transcribe`, `remove-background`), invoke the `hyperframes-media` skill instead.
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.