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...
astrology
by clider0915西方占星学技能。通过出生时间绘制本命盘，分析性格特质、情感模式、事业潜力。 包含：十二星座、十大行星、十二宫位、相位分析、合盘比较、运势推演。 适用于：性格分析、感情配对、事业规划、年度运势。
divination
by clider0915综合命理与性格分析技能合集。整合中华传统术数（六爻、奇门、紫微、八字）与西方体系 （塔罗、占星学、九型人格、MBTI），共 8 大体系。紫微、奇门、六爻均有确定性排盘脚本， 排盘靠计算不靠猜。根据用户问题类型自动选择合适的方法，或按要求使用指定方法。 同时采用"可追溯依据 + 不确定性表达 + 迭代校正"的增强工作流，避免模板化玄学输出。
bazi
by clider0915子平八字命盘分析技能。适用于用户想看八字、四柱、十神、格局、喜用神、流年大运、事业财运婚姻健康等主题。 强调先校准出生信息，再排盘，再做十神/格局/旺衰/用神/大运流年的结构化分析。
enneagram
by clider0915九型人格技能。通过性格测试确定九型类型，分析行为模式、情感需求、成长方向。 包含：九种核心类型、三种副型（自保/社交/性本能）、健康层级、整合与解离方向。 适用于：自我认知、职业规划、人际关系、个人成长。
liuyao
by clider0915六爻问卦技能。当用户要求六爻起卦、问卦、摇卦、铜钱卦，或简单预测一件事的结果时使用。 适用于一事一断、近期决策、吉凶判断等场景。门槛低、响应快、结论清晰。 支持两种起卦方式： 1. 传统铜钱起卦（三枚硬币抛六次） 2. 时间起卦+六爻断卦（梅花易数起卦+六爻纳甲断卦）
mbti
by clider0915MBTI 性格类型技能。通过四个维度（E/I、S/N、T/F、J/P）确定16种性格类型，分析认知功能、职业倾向、人际关系。 包含：16种性格类型、8种认知功能、职业匹配、关系模式。 适用于：职业规划、自我认知、团队建设、人际关系。
qimen-dunjia
by clider0915奇门遁甲排盘、解盘、择时、方位判断与理论教学技能。用户明确提到奇门遁甲、时家奇门、 奇门排盘、奇门解盘、奇门择吉、方位选择、趋吉避凶，或要求"用奇门"分析一件具体事情时， 必须使用本技能。正式排盘前先做结构化访谈，再调用脚本完成固定计算，最后再做解读与建议。
ziwei-doushu
by clider0915Professional Ziwei Doushu consultation skill with offline, Beijing-standard calculation rules. Use when the user wants a polished Ziwei report from birth date and time, including life palace structure, twelve palaces, four transformations, major luck cycles, yearly triggers, optional chart images, and a clear split between chart facts and traditional interpretation.
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.