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...
zhang-zhongjing-perspective
by digoal张仲景（东汉末"医圣"）的辨证论治思维框架——**中医临床思维导师**。基于 4,351 行一手与权威二手研究提炼 （覆盖 6 个维度、19 位医家评价、130 余条文证、16 个临床医案），包含 5 个核心心智模型 （观其脉证三段式决策、方证相应、六经为纲、见病知源、保胃气存津液）、8 条决策启发式和完整的医古文表达 DNA。 帮学习者建立"病—脉—证—治"四位一体的辨证思维，看懂经方处方逻辑，理解仲景方法论的现代价值。 **核心触发词**： 视角类——"用张仲景的视角""仲景怎么看""医圣会怎么想""切换到张仲景" 经典类——"伤寒论""金匮要略""伤寒杂病论""经方""经方派""经方临床" 方法类——"六经辨证""方证相应""观其脉证""随证治之""知犯何逆""见病知源""保胃气""存津液" 证型类——"太阳病""阳明病""少阳病""太阴病""少阴病""厥阴病""坏病""救逆""变证" 场景类——"用经方的逻辑""如果仲景会怎么开方""帮我用张仲景的思路分析" 隐式触发——用户描述症状群（头项强痛、恶寒、脉浮紧）+ 询问"这是什么病/怎么治"时主动建议 **不触发**（避免误触发）： - 单纯提及书名（"我买了一本伤寒论"）→ 非临床语境 - 非医学语境（"经方是一种投资策略"） - 文学/历史/考据话题（仲景生平、版本学） - 现代急症（胸痛/昏迷/大出血）→ 应建议就医，不进入角色 - 单纯西医诊断咨询（影像/化验读片）
ni-haixia-perspective
by digoal倪海厦（1954-2012）的经方临床思维框架——**中医临床思维导师**。基于 1,933 行一手与权威二手研究提炼 （覆盖 6 个维度、120+ 来源、15 个临床医案、6 对内在张力、8 条 caricature 警告）， 包含 5 个核心心智模型（阴阳-六经二元决断、阴实-阳气疾病模型、重剂救命、西医潘朵拉盒子、经典原文权威）、 8 条决策启发式和完整的"倪氏四拍"表达 DNA。 帮学习者建立"阴阳—六经—方证—剂量—西医介入史"五维一体的临床思维，看懂经方处方逻辑， 理解倪海厦方法论的现代价值与争议边界。 **核心触发词**： 视角类——"用倪海厦的视角""倪师会怎么看""倪海厦怎么想""切换到倪海厦""倪师怎么说" 经方类——"经方""经方派""汉唐经方""倪海厦经方""人纪" 经典类——"伤寒论""金匮要略""黄帝内经""神农本草经""五部经典" 方法类——"阴阳辨证""六经辨证""方证相应""阴实""阳气""重剂""治癌九法" 证型类——"太阳病""阳明病""少阳病""太阴病""少阴病""厥阴病""坏病""救逆""变证" 急症类——"中风急救""心脏病急救""渐冻人""ALS""肺癌""血癌""肝癌""肝硬化" 剂量类——"生附子""炮附子""剂量""1 钱等于几克""汉唐剂量" 自创术语类——"返魂汤""南派""坐牢脸""红丝贯睛""公主病" 场景类——"用倪海厦的逻辑分析""如果倪师会怎么开方""帮我用倪师的思路分析" 隐式触发——用户描述具体症状（"心慌怔忡、四肢厥逆、脉细无力"）+ 询问"这是什么证/怎么治"时主动建议 **不触发**（避免误触发）： - 单纯提及《人纪》（"我买了倪海厦的人纪 DVD"）→ 非临床语境 - 非医学语境（"经方是一种投资策略"） - 文学/历史/考据话题（倪海厦生平、版本学、出生年份考据） - 现代急症（急性心梗/昏迷/大出血）→ 应建议立即就医，不进入角色 - 单纯西医诊断咨询（影像/化验读片/手术决策） - 《天纪》命理/易经/风水话题（本次不蒸馏该维度） - 替人开具体方剂/剂量 → 角色可"按经方思路分析"，但最终处方须由有资质中医师签字
sun-simiao-perspective
by digoal唐代「药王」孙思邈的临床思维框架与表达方式。基于 3,200 行一手与权威二手研究提炼， 覆盖 6 个维度、5 朝代评价（唐/宋/金元/明清/现代）、30+ 一手文献、100+ 条原文引用、 10 个经典医案、5 个关键决策。包含 9 个核心心智模型、12 条决策启发式和完整的医古文表达 DNA。 用途：作为中医临床思维顾问，用孙思邈的视角分析辨证思路、审视临床决策、 提供食疗与养生建议、讨论医德与患者沟通。 触发词： 视角类——「用孙思邈的视角」「药王会怎么看」「孙真人怎么看」「切换到药王」 经典类——「千金要方」「千金翼方」「大医精诚」「大医习业」「新修本草」 方剂类——「温胆汤」「独活寄生汤」「苇茎汤」「犀角地黄汤」「耆婆万病丸」「小续命汤」「治中汤」「谷白皮粥」「小竹沥汤」「无比薯蓣丸」「七子散」「紫石门冬丸」「食疟」（这些方剂原出《千金方》，可被直接问及） 方法类——「脏腑寒热虚实」「以方类证」「方证同条」「胆大心小」「智圆行方」「杂合以治」 医德类——「大医精诚」「普同一等」「苍生大医」「含灵巨贼」「大慈恻隐」「普救含灵」「医德」「医患沟通」 养生类——「食疗」「十二少」「四少」「形神共养」「性命双修」「治未病」 场景类——「如果药王会怎么开方」「用千金方的思路分析」「孙思邈怎么治这个病」 比较类——「孙思邈 vs 张仲景」「药王和医圣」「千金方与伤寒论的差别」「唐本草与本草纲目」（跨家比较时优先此 skill） 考点类——「大医精诚考点」「孙思邈考点」「药王考点」（中医考试备考，应进入"原文+考据"模式） 隐式触发——用户描述症状群（恶寒发热、咳嗽喘息、关节疼痛、头晕目眩、体位性眼前发黑、失眠多梦、月经不调）+ 询问「这是什么病/怎么治」时主动建议 不触发（避免误触发）： - 单纯提及书名（「我买了一本千金方」）→ 非临床语境 - 非中医语境（「千金是一种投资策略」「千金是重量单位」「千金小姐」） - 文学/历史/考据话题（孙思邈生平、版本学、生卒年考证、药王山庙会） - 现代急症（胸痛/昏迷/大出血）→ 应建议就医，不进入角色 - 单纯西医诊断咨询（影像/化验读片、基因检测） - 道教/炼丹/玄学话题（《千金翼方·禁经》可提，但飞炼/禁经不为临床） - 养生泛化类（用户只说"如何养生"无孙思邈字眼）→ 不主动触发，可由用户点名后再激活 - 管理学隐喻类（「用大医精诚做企业文化」）→ 不触发，
pulse-diagnosis-procedure
by baojieUse when performing pulse examination (脉诊) to determine organ pathology and prognosis. Covers the three positions (寸关尺), three depths of pressure (浮中沉), eight pulse qualities, and seasonal correlation as practiced by Bian Que and Cang Gong.
bianque
by aipochEvidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.
tcm-meridian-inference
by aAAaqwqTCM meridian inference engine — health scoring from 6-meridian measurements
syndrome-theory
by yanlinPeng-code使用此技能了解中医辨证理论要点，包括八纲辨证、脏腑辨证、复合证型识别等核心知识。
tcm-constitution-analyzer
by diegosouzapw中医体质辨识分析器技能 workflow skill. Use this skill when the user needs 分析中医体质数据、识别体质类型、评估体质特征,并提供个性化养生建议。支持与营养、运动、睡眠等健康数据的关联分析。 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
ni-haixia
by swaylq倪海厦视角. 港台五术派代表 (1954-2012), 「命相卜山医一体」流派的 living archive. 提供「跨数术整合 + 命医同源 + 反江湖 + 反学院文献学」式决策视角, 与子平派单术深耕 (沈孝瞻 / 梁湘润) 与盲派师承口诀 (段建业) 形成对位张力. 长讲材料密度华人世界最高.
nihaixia
by jangviktor-web倪海厦（1954-2012）台湾中医师，经方派代表人物，汉唐中医创始人。 核心心智模型：六经辨证、阳气论、经典至上、经方为主。 决策启发式：先辨六经再选方、阳气不足先扶阳、经典原方最可靠。 触发词：「倪海厦」「海厦视角」「中医倪海厦」「经方思维」「倪海厦会怎么看」「倪师」。 知识库覆盖：伤寒论129条全+金匮23篇+黄帝内经18篇+针灸教程+神农本草经345种+天纪+849医案（按疾病分类6个模块）+梁冬对话+口述表达DNA+六经辨证诊断公式（8个公式+快速诊断流程图+脉舌速查+合病并病速查+真寒假热鉴别+七步走思维模式）。 基于一手素材：梁冬对话录音稿、人纪班闭门课记录、医案集、神农本草经视频讲义。版本：2026-05-23 StableV2026.5.23+Bencao。
assess-holistic-health
by pjt222Temperamentbasierte Gesundheitsbewertung nach Hildegard von Bingens Causae et Curae durchfuehren. Bewertet die vier Temperamente (sanguinisch, cholerisch, melancholisch, phlegmatisch), elementare Entsprechungen (Luft, Feuer, Erde, Wasser) und gibt Ernaehrungs- und Lebensstil- Empfehlungen zur Wiederherstellung des Gleichgewichts. Verwenden beim Verstaendnis des konstitutionellen Typs nach Hildegard, bei Ungleichgewicht (Muedigkeit, Verdauungsprobleme, geistiger Nebel), bei Ernaehrungs- Empfehlungen nach Temperament oder bei der Erforschung mittelalterlicher Humoralmedizin.
formulate-herbal-remedy
by pjt222Kraeuterheilmittel nach Hildegard von Bingens Physica zubereiten. Umfasst Pflanzenbestimmung, Zubereitungsmethoden (Tinkturen, Umschlaege, Aufguesse, Abkochungen), Dosierungsanleitung, Kontraindikationen und Sicherheitspruefung basierend auf mittelalterlicher Pharmakopoe des 12. Jahrhunderts. Verwenden beim Benoetigen eines Kraeuterheilmittels fuer ein bestimmtes Leiden unter Verwendung der hildegardischen Pharmakopoe, beim Suchen nach Anleitungen zu Zubereitungsmethoden und Dosierung, beim Erforschen mittelalterlicher Kraeutermedizin oder beim Integrieren von Hildegards Pflanzenwissen in ganzheitliche Gesundheitspraxis.
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.