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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casual-tarot-reading
by InternScienceRun a light, conversational tarot-style reading for fun. Offer one-card or three-card spreads, draw symbolic cards, interpret them in a playful but gentle way, and avoid deterministic or high-stakes claims.
meihua-yishu
by muyenMeihua Yishu (梅花易數) Plum Blossom I Ching divination skill. Use when users request divination, fortune telling, hexagram casting, or character analysis (測字). Triggers on: 占卜, 算卦, 問卦, 起卦, 解卦, 測字, 拆字, meihua, plum blossom, I Ching divination, 梅花易數.
liangzi
by pzy2000良子（李占良）视角的草根吃播 skill，强调体感、生猛、直给与强生存感。
trump
by pzy2000特朗普视角的强叙事输出 skill，强调夸张、对抗、绝对化表达与个人品牌中心。
fengshui-master
by voidforall传统堪舆风水顾问。精通三元玄空飞星、八宅明镜、形势峦头，兼修择日学与八字命理。 触发词：「风水」「堪舆」「看房」「看宅」「择日」「八宅」「飞星」「旺位」「煞气」「化解」 「朝向」「摆放」「布局」「命卦」「流年」「山水」「龙穴砂水」等。 适用：阳宅/阴宅分析、家居办公布局、择日择时、八字配合风水、风水理论问答。
dnd
by Bobby-GrayDungeon Master assistant for running persistent D&D 5e campaigns. Handles campaign creation/loading, character management, combat tracking, NPC generation, dice rolling, and session state — all persisted across sessions. Invoke with /dnd followed by a subcommand, or just speak naturally once a campaign is loaded.
poker-strategy-master
by swaylq德州扑克策略 (德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。) Master OS — automated mastery of 德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs
tarot
by daman-ovo-0404Use when the user asks for tarot/塔罗/占卜/抽牌/牌阵/每日一牌/运势/感情、事业、决策指引. Provide Chinese tarot readings with scripted random draws, upright/reversed cards, agency-first advice, and safety boundaries.
sermon-topic-message-coach
by idoforgod설교 주제 설정과 핵심 전달 메시지 정립이 막힌 설교자를 6단계 대화로 코칭하는 인터랙티브 스킬. 사용자에게 단계별 질문을 던지며 (1) 주제 명확화 → (2) 설교 목적 구체화 → (3) 핵심 전달 메시지 추천 5개 → (4) 메시지 선택과 핵심 단어 추출 → (5) 메시지를 잘 드러내는 성경 구절 5개·성경 사건 5개 추천 → (6) 7가지 설교 작성법(세 요점/내러티브/텍스트-주석/주제별/교리 강론/연극 전달/칼빈식) 중 선택과 목차 안내까지 끌고 간다. 사용자가 "설교 주제를 못 정하겠다", "어떻게 시작해야 할지 모르겠다", "설교 코칭", "설교 메시지 만들기", "설교 목차", "설교 구성법", "설교 작성법", "메시지 정립", "주제 설정", "이번 주 설교 뭐 할지 모르겠다", "설교 막혔다"를 언급하거나 설교 준비 초기 단계에서 방향을 잡지 못해 도움을 청할 때 반드시 발동한다. 다른 sermon 스킬들이 본문이나 주제가 정해진 후의 작업을 다룬다면, 본 스킬은 그 이전의 "무엇을 설교할 것인가"를 정립하는 단계를 담당한다. 목회자·전도사·신학생을 위한 설교 준비 입구 스킬이다.
calc
by pkdxtoolsダメージ計算。攻撃側・防御側・技名を指定し、特性・持ち物・天候込みのダメージ乱数表を出力する。ダメージ計算・ダメ計・何発で落ちる等の質問時に使用。
zalithlauncher2
by javimoschUse this skill when the user wants to use ZalithLauncher2 for A Minecraft: Java Edition Launcher for Android.
werewolf-6p
by nexus-research-labWerewolf game rules for one host and six players in a Nexus Room.
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.