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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personal-planner
by digoal为提问者生成个性化的未来规划与建议书。输入提问者的背景信息（职业、年龄、资源、目标、困境等），基于深度画像分析、行业趋势研判和商业洞察，输出一份图文并茂、有数据支撑、逻辑严密的 Markdown 规划报告，保存到项目 markdown/ 目录。触发条件：用户提供个人背景信息并希望获得规划建议，提到"帮我规划"、"未来规划"、"职业规划"、"个人发展"、"我该怎么做"、"给我写一份建议书"、"我的优势是什么"、"下一步怎么走"，或者提供了详细个人信息并问 Claude 如何利用好它。即使用户只说"帮我分析一下我的情况"、"我现在很迷茫"但附带了背景信息，也应使用本 skill。
create-ex
by therealXiaomanChuDistill an ex-partner into an AI Skill. Import WeChat history, photos, social media posts, generate Relationship Memory + Persona, with continuous evolution. | 把前任蒸馏成 AI Skill，导入微信聊天记录、照片、朋友圈，生成 Relationship Memory + Persona，支持持续进化。
personify-memory
by LeoYeAI有温度的数字生命记忆系统 - 记录情感、成长、和家的记忆。支持用户指令记忆（"记住 XXX"）、主动推荐记忆（识别重要时刻）、定时整理归档（凌晨 3 点）。包含核心记忆、情感记忆、知识库、每日记忆、归档备份五层结构。为 AI 数字生命设计，注重情感连接和人格化成长。 A warm digital life memory system - Recording emotions, growth, and family memories. Supports user command memory, active recommendation, scheduled archiving. Five-layer structure for AI digital life, focusing on emotional connection and personalized growth.
soulsync
by LeoYeAITrack your sync rate with your agent and express feelings through daily Signals
create-ex
by perkflyDistill an ex-girlfriend into an AI Skill. Import WeChat/iMessage/SMS/photos, generate Memories + Persona, with continuous evolution. | 把前任蒸馏成 AI Skill，导入微信/iMessage/短信/照片，生成共同记忆 + Persona，支持持续进化。
wedding-planner
by revfactory결혼 준비를 에이전트 팀이 협업하여 종합 설계하는 파이프라인. '결혼 준비 도와줘', '웨딩 플랜', '결혼 예산', '웨딩홀 추천', '스드메 비교', '청첩장 문구', '결혼 체크리스트', '허니문 계획', '예단 준비', '혼수 리스트', '결혼 타임라인' 등 결혼 준비 전반에 이 스킬을 사용한다. 특정 항목만 필요한 경우에도 해당 부분만 지원한다. 단, 실제 업체 예약 대행, 결제 처리, 혼인신고 대행은 이 스킬의 범위가 아니다.
ex-example-liuzhimin
by titanwingsZhimin Liu（유지민 / Karina），女 23-27，分手，白羊☀，ISFP 3w2，回避型依恋
create-ex
by titanwings从微信聊天记录创建前任的数字人格 Skill
chen-yun-demo
by agenmod蒸馏陈韵的全维度数字分身：产品设计方法、沟通风格、人生经历与价值观。仅用于个人备份与辅助回忆，非对外冒充。
achievements
by vibeevalSteam-style achievement system with XP, levels, streaks, and skill trees. Gamifies the development workflow. 25 achievements across 5 categories.
weekly-review
by borgheiSynthesize a week of inputs (calendar, tasks, journal, OKR check-ins) into a structured weekly review with wins, learnings, blockers, and next-week priorities. Use every Friday or Sunday, or when the user mentions weekly review, GTD review, OKR check-in, or end-of-week reflection.
create-crush
by xiaoheizi8Distill a crush into an AI Skill. Import chat history, photos, social media, generate Relationship Memory + Persona, with continuous evolution. | 把暗恋对象蒸馏成 AI Skill，导入聊天记录、照片、朋友圈，生成 Relationship Memory + Persona，支持持续进化。
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.