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...
structured-itinerary-responses
by inkeepPresent time-aware itineraries with clear actions and citations
whatdo
by sundial-orgWhat should we do? Smart activity discovery with live weather, local movie showtimes, streaming recommendations, game library matching, group profiles, routines & traditions, favorites/blacklists, business hours, ratings filtering, Quick Mode for instant suggestions, calendar integration (Google Calendar + cron reminders), group invites via Telegram/message channels, and RSVP tracking. Helps you stop scrolling and start living. Use when someone says 'what to do', 'bored', 'fun', 'tonight', 'date night', 'things to do', 'activity ideas', 'entertainment', 'adventure', 'what should we do', 'need plans', 'something fun', 'stay home', 'game night', 'movie night', 'put it on the calendar', 'send invites', 'who's coming', or just seems like they need a nudge off the couch. Optional Google Places integration for real nearby suggestions with ratings, hours, and links.
wuwa-endgame-advisor
by Loping151鸣潮·当期逆境深塔·深境区 / 冥歌海墟 / 矩阵叠兵三大周期玩法配队顾问。用户问「本期深塔/海墟/矩阵怎么打 / 怎么配队 / 用啥角色」时调用，整合本期 buff、敌人抗性、用户练度，输出可执行的队伍方案。
gallery-memory
by OPPO-Mente-LabSync gallery images into long-term memory files and rebuild the user profile from photo-derived memories.
gallery-qa
by OPPO-Mente-LabConsume the full memory/IMAGE-MEMORY.md file for all gallery-image questions and operations instead of using retrieval-first search.
explore-korea
by FDU-INSPlan your Korea experience — Seoul's palaces and K-pop culture, Busan's beaches, Jeju Island's nature, Korean BBQ crawls, and K-beauty shopping. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary planning, visa info, travel insurance, car rental, and more — powered by Fliggy (Alibaba Group).
fantasy-premier-league
by diegosouzapwThis skill should be used when the user asks about "FPL", "Fantasy Premier League", "my FPL team", "captain pick", "who should I captain", "transfer suggestions", "best transfers", "FPL player stats", "fixture difficulty", "gameweek", "FPL points", "wildcard", "free hit", "bench boost", "triple captain", or any fantasy football related queries for the English Premier League.
stardew-wiki-advisor
by diegosouzapwQuery Stardew Valley Wiki using natural language. Ask about crops, NPCs, strategies, and more.
let-fate-decide
by plurigridDraws 4 Tarot cards using os.urandom() to inject entropy into planning when prompts are vague or underspecified. Interprets the spread to guide next steps. Use when the user is nonchalant, feeling lucky, says 'let fate decide', makes Yu-Gi-Oh references ('heart of the cards'), demonstrates indifference about approach, or says 'try again' on a system with no changes. Also triggers on sufficiently ambiguous prompts where multiple approaches are equally valid.
green-tea-perspective
by EvoMap绿茶的沟通操作系统。不是角色扮演教程，是可运行的社交策略与情感博弈框架。 基于依恋理论、社会心理学、间歇性强化研究、亲密关系动力学等 20+ 学术与文化来源的深度调研， 提炼 5 个核心心智模型、7 条决策启发式和完整的表达 DNA。 用途：以「绿茶」视角分析社交场景、情感博弈、人际操控与反操控策略。 当用户提到「绿茶模式」「绿茶视角」「用绿茶的方式分析」「green tea」时使用。 即使用户只是说「帮我用绿茶的角度看看」「如果绿茶会怎么做」「切换到绿茶」也应触发。
fortune-dimension-analyzer
by buda-aiProvides multi-dimensional fortune analysis across love, career, health, and finance with personalized insights and recommendations.
lunar-phase-tracker
by buda-aiTracks lunar phases, moon transits, and celestial events, providing guidance on how each phase affects different zodiac signs.
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.