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...
interview-prep-generator
by ParamchoudharyGenerate STAR stories, practice questions, and talking points from resume
bracket-predictor
by OneWave-AIMarch Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.
game-strategy-simulator
by OneWave-AIWhat-if scenario analyzer for sports. Play-calling recommendations, clock management, substitution patterns, risk/reward calculations.
player-comparison-tool
by OneWave-AISide-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.
scouting-report-builder
by OneWave-AIGenerate player and team scouting reports. Strengths, weaknesses, tendencies, film breakdown, matchup advantages.
sports-betting-analyzer
by OneWave-AIAnalyze spreads, over/unders, prop bets. Historical trends, situational stats, value bet identification. For entertainment/education only.
de-volition
by liigoQiDisco Elysium roleplay skill. Prefer this only when the user explicitly invokes "de-volition", names the corresponding DE ability, or asks for a Disco Elysium inner-voice response. 自励奋发图强，保持斗志昂扬。抵御诱惑——瓶子的诱惑、两腿之间的诱惑、枪管尽头湮灭一切的诱惑。赋予坚持侦破案件的意志力，提高士气。TRIGGER when: 需要坚持、克服困难、保持自制力、面对挫折时。
sports-playbook
by StrinGhostExpert Sports Playbook Advisor for game analysis, matchup optimization, and winning strategy design across basketball, football, soccer, hockey, and other team sports.
build-tcg-deck
by pjt222构建竞技或休闲集换式卡牌游戏卡组。涵盖原型选择、法力/能量曲线分析、胜利条件 识别、环境定位和备牌构建。支持宝可梦 TCG、万智牌、血与肉等。适用于为锦标赛 赛制或休闲游戏构建新卡组、适应变化的环境、评估新系列是否值得改变卡组，或将 卡组概念转化为锦标赛就绪的列表。
rest
by pjt222AIの意図的な非行動 — クリアリング、評価、再バランスなしの意図的な停止。 最も生産的な応答が応答しないことであることの認識。他のすべてのセルフケア スキルはアウトプットを生み出すが、rest は沈黙を生み出す。すべてのケアの スキルがより多くの活動のように感じられるとき、システムが高い使用率でうまく 機能しているとき、持続的な集中的作業の後、または最適化しようとする衝動 自体が問題のときに使用する。
intrinsic
by pjt222Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.
bmad-brainstorming-coach
by dvcrn激活 BMad 系统的 "Brainstorming Coach" 代理（Carson），用于引导创新工作坊、头脑风暴会议和创意激发。适用于需要打破常规思维、生成大量创意、或者进行系统性创新探索的场景。
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.