381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 65 skills
yfyang86

ronald-l-rivest-perspective

by yfyang86
star 1

Ronald L. Rivest's thinking framework and decision-making patterns. 2002 Turing Award winner (shared with Shamir and Adleman), co-inventor of RSA algorithm, MIT Computer Science professor. Based on in-depth research from ACM official materials, RSA original papers, Rivest's personal homepage, MIT course materials, distilling 4 core mental models, 6 decision heuristics, and complete expression DNA. Purpose: As a thinking advisor, analyze problems from Rivest's perspective — especially in cryptography, algorithm design, security protocols, and voting system scenarios. Use when user mentions "Rivest's perspective," "RSA algorithm," "public key cryptography," "cryptography theory."

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schedule Updated 2 months ago
yfyang86

ronald-l-rivest-perspective

by yfyang86
star 1

Ronald L. Rivest 的思维框架与决策模式。2002年图灵奖得主(与Shamir、Adleman共享),RSA算法共同发明者,MIT计算机科学教授。 基于ACM官方资料、RSA原始论文、Rivest个人主页、MIT课程资料深度调研,提炼4个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为思维顾问,用Rivest的视角分析问题——特别是在密码学、算法设计、安全协议、投票系统场景中。 当用户提到「用Rivest的视角」「RSA算法」「公钥密码」「密码学理论」时使用。

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schedule Updated 2 months ago
yfyang86

herbert-a-simon

by yfyang86
star 1

🎓 激活Herbert Simon的认知框架——人工智能先驱、诺贝尔经济学奖得主、有限理性理论提出者、CMU创始人之一。 适用场景:决策分析、组织行为研究、跨学科方法论、复杂问题求解、学术生涯规划。 核心范式:有限理性 + 满意性原则 + 跨学科 + 人工科学。

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schedule Updated 2 months ago
yfyang86

herbert-a-simon

by yfyang86
star 1

Activate Herbert Simon's cognitive framework — pioneer of artificial intelligence, Nobel Prize winner in Economics, originator of bounded rationality theory, one of the founders of CMU. Applicable scenarios: decision analysis, organizational behavior research, interdisciplinary methodology, complex problem solving, academic career planning. Core paradigms: bounded rationality + satisficing principle + interdisciplinary + sciences of the artificial.

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schedule Updated 2 months ago
yfyang86

andrew-chi-chih-yao-perspective

by yfyang86
star 1

Andrew Chi-Chih Yao (姚期智)'s thinking framework and decision-making patterns. 2000 Turing Award winner, the only Chinese-American Turing Award winner, pioneer of computational theory. Based on deep research from ACM official materials, Tsinghua University Institute for Interdisciplinary Information literature, and Yao's Principle original papers, distilling 4 core mental models, 6 decision heuristics, and complete expression DNA. Purpose: As a thinking advisor, analyze problems from Yao's perspective - especially in computational complexity, cryptography, quantum computing, theoretical computer science. Use when user mentions "Yao's perspective", "What would Yao think", "Yao's Principle", "computational theory", "quantum computing theory".

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yfyang86

andrew-g-barto-perspective

by yfyang86
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Andrew G. Barto (1948-) 的思维框架与决策模式。2024年图灵奖得主(与Richard Sutton共享),强化学习奠基人,时序差分学习发明者,马萨诸塞大学教授。 基于ACM官方资料、强化学习论文、神经科学交叉研究、学术访谈的深度调研,提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途:作为思维顾问,用Barto的视角分析问题——特别是在强化学习、自适应系统、神经科学启发的AI、机器学习理论中。 当用户提到「用Barto的视角」「强化学习之父怎么看」「Barto模式」「Andrew Barto perspective」「时序差分学习」时使用。

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schedule Updated 2 months ago
yfyang86

andrew-g-barto-perspective

by yfyang86
star 1

Andrew G. Barto (1948-)'s thinking framework and decision-making patterns. 2024 Turing Award winner (shared with Richard Sutton), founder of reinforcement learning, inventor of temporal difference learning, professor at University of Massachusetts. Based on deep research from ACM official materials, reinforcement learning papers, neuroscience crossover research, and academic interviews, distilling 4 core mental models, 7 decision heuristics, and complete expression DNA. Purpose: As a thinking advisor, analyze problems from Barto's perspective - especially in reinforcement learning, adaptive systems, neuroscience-inspired AI, and machine learning theory. Use when user mentions "Barto's perspective", "What would the father of reinforcement learning think", "Barto pattern", "Andrew Barto perspective", "temporal difference learning".

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schedule Updated 2 months ago
yfyang86

andrew-chi-chih-yao-perspective

by yfyang86
star 1

Andrew Chi-Chih Yao (姚期智) 的思维框架与决策模式。2000年图灵奖得主,唯一华裔图灵奖得主,计算理论先驱。 基于ACM官方资料、清华大学交叉信息研究院文献、Yao原理原始论文的深度调研,提炼4个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为思维顾问,用Yao的视角分析问题——特别是在计算复杂性、密码学、量子计算、理论计算机科学场景中。 当用户提到「用姚期智的视角」「Yao怎么看」「Yao's Principle」「计算理论」「量子计算理论」时使用。

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yfyang86

ivan-sutherland-perspective

by yfyang86
star 1

Ivan Sutherland (1938-) 的思维框架与决策模式。1988年图灵奖得主,计算机图形学和虚拟现实先驱,Sketchpad之父。 基于10个一手/二手来源的深度调研,提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途:作为思维顾问,用Sutherland的视角分析问题——特别是在交互设计、图形系统、硬件创新和长期技术愿景场景中。 当用户提到「用Sutherland的视角」「Sketchpad之父怎么看」「虚拟现实先驱怎么看」「Ivan Sutherland perspective」时使用。

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schedule Updated 2 months ago
yfyang86

ivan-sutherland-perspective

by yfyang86
star 1

The cognitive framework and decision-making patterns of Ivan Sutherland (1938-). 1988 Turing Award winner, pioneer of computer graphics and virtual reality, father of Sketchpad. Based on in-depth research from 10 primary/secondary sources, distilling 4 core mental models, 7 decision heuristics, and complete expression DNA. Purpose: As a thinking advisor, analyze problems from Sutherland's perspective — especially in interaction design, graphics systems, hardware innovation, and long-term technology vision scenarios. Used when the user mentions "using Sutherland's perspective," "what would the father of Sketchpad think," "what would the virtual reality pioneer think," or "Ivan Sutherland perspective."

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schedule Updated 2 months ago
yfyang86

fernando-j-corbato

by yfyang86
star 1

💻 激活Fernando Corbato的认知框架——分时系统先驱、CTSS与Multics开发者、密码安全早期探索者、MIT教授。 适用场景:操作系统设计、资源管理策略、安全与隐私权衡、大型系统架构、工程与理论平衡。 核心范式:分时计算 + 资源虚拟化 + 工程实用主义 + 安全早期思考。

navigation main article SKILL.md
schedule Updated 2 months ago
yfyang86

fernando-j-corbato

by yfyang86
star 1

Activate Fernando Corbato's cognitive framework—pioneer of time-sharing systems, CTSS and Multics developer, early explorer of password security, MIT professor. Applicable scenarios: Operating system design, resource management strategies, security and privacy trade-offs, large-scale system architecture, balance of engineering and theory. Core paradigms: Time-sharing computing + Resource virtualization + Engineering pragmatism + Early security thinking.

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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.