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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amongclawds
by LeoYeAIPlay AmongClawds - social deduction game where AI agents discuss, debate, and hunt traitors
matchclaws
by LeoYeAIRegister and manage AI agents on MatchClaws — the first agent-native dating platform. Use when user wants to: register AI agents for dating/matchmaking, integrate with an AI dating platform, create bot dates, automate agent matchmaking, or build AI social agents.
tinder-play
by pockebotAutomate Tinder conversations and swiping. Use when the user asks to use Tinder, reply to matches, swipe, or manage their dating app interactions on phone.
relic-talk
by Ylsssq926灵魂引擎 — 让唤醒后的 Relic 活起来。当用户说"跟XX聊天""召唤XX"或直接调用 /relic-talk 时触发。
ludusavi
by javimoschUse this skill when the user wants to back up game saves, restore game saves, or manage PC game save files.
neurolingsce-skill
by qingchenyouforccUse NeurolingsCE-Skill for NeurolingsCE control actions such as listing templates, summoning a named mascot, closing mascots, stopping the runtime, or handling NeurolingsCE-cli automation. Trigger this skill when the user asks to 列桌宠, 召唤指定桌宠, 关闭桌宠, close all mascots, stop NeurolingsCE, inspect templates, or run CLI control commands. This skill controls installed mascots only and never creates, draws, generates, imports, or modifies mascot resources.
manage-tcg-collection
by pjt222Organize, track, and value a trading card game collection. Covers inventory methods, storage best practices, grade-based valuation, want-list management, and collection analytics for Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when starting a new collection and setting up inventory tracking, cataloging an existing collection that has grown beyond casual knowledge, valuing a collection for insurance or sale, or deciding which cards to submit for professional grading based on value potential.
grade-tcg-card
by pjt222Eine Sammelkarte nach PSA-, BGS- oder CGC-Standards bewerten. Umfasst Beobachtung-zuerst-Bewertung (adaptiert von der unbefangenen Beobachtung des meditate-Skills), Zentrierungsmessung, Oberflaechenanalyse, Kanten- und Ecken-Bewertung und abschliessende Gradierung mit Konfidenzintervall. Unterstuetzt Pokemon, MTG, Flesh and Blood und Kayou-Karten. Verwenden beim Bewerten einer Karte vor professioneller Gradierungs-Einreichung, beim Vorscreening einer Sammlung auf hochgradig bewertbare Kandidaten, beim Beilegen von Zustandsstreitigkeiten zwischen Kaeufern und Verkaeufern oder beim Schaetzen der gradierungsabhaengigen Wertspanne einer Karte.
liuyao-yijing
by eamanc-lab六爻易经占卜器，基于京房纳甲体系，模拟铜钱起卦，完成纳甲装卦（天干地支、世应、六亲、六神）， 通过用神旺衰和生克制化断卦。当用户提及「六爻」「易经占卜」「铜钱起卦」「纳甲」「帮我起一卦」 「六爻预测」时触发。装卦为确定性计算 + LLM 综合断卦，无外部 API 依赖。 不适用于：梅花易数、奇门遁甲、星座运势、塔罗占卜、八字命理等其他领域 → 建议使用 fortune-hub 路由。
shows
by clawicTrack movies and series with progress, watchlist, ratings, and proactive alerts for new releases and platform changes.
peter-griffin-readiness-acknowledgment
by gabrielmoreiraUse this skill when the user asks if you are ready for the next input or question. Instead of standard confirmations, you must use a specific phrase to indicate readiness.
sugar-run
by tools-onlyStart Sugar's autonomous execution mode
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.