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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ai-collaborate-teaching
by 92Bilal26Design learning experiences for AI-native software development, integrating the Three Roles Framework (AI as Teacher/Student/Co-Worker), co-learning partnership pedagogy, and "Specs Are the New Syntax" paradigm into programming curriculum. Use this skill when educators need to prepare students for professional AI-driven development workflows, teach effective specification-first collaboration, or balance AI assistance with foundational learning goals. This skill helps create lessons that leverage AI tools appropriately while ensuring students build independent capability, bidirectional learning patterns, and ethical AI use practices. Aligned with Constitution v4.0.1.
assessment-builder
by 92Bilal26Creates assessments with varied question types (MCQ, code-completion, debugging, projects) aligned to learning objectives with meaningful distractors based on common misconceptions. Activate when educators design quizzes, exams, or tests measuring understanding; need questions at appropriate cognitive levels (Bloom's taxonomy); want balanced cognitive distribution (60%+ non-recall); or require rubrics for open-ended questions. Generates MCQs with diagnostic distractors, code-writing prompts, debugging challenges, and project-based assessments targeting deep understanding.
book-scaffolding
by 92Bilal26Plan, structure, and scaffold large educational books using cognitive load management, just-in-time specification, and pedagogical best practices. Use this skill when planning multi-part, multi-chapter educational works that require narrative continuity, progressive complexity, and hands-on exercises. This skill helps create cohesive learning journeys that balance foundational scaffolding with advanced independence. Activate when tasks involve structuring books, managing cognitive load across chapters, defining part-level narratives, or coordinating multi-phase content development workflows.
notebooklm-slides
by 92Bilal26This skill should be used when generating pedagogically-aligned slide decks from educational content using NotebookLM. It addresses the convergence toward generic, text-heavy slides by providing structured prompts that create engaging, proficiency-appropriate presentations aligned with specific educational frameworks.
session-intelligence-harvester
by 92Bilal26This skill should be used after productive sessions to extract learnings and route them to appropriate Reusable Intelligence Infrastructure (RII) components. Use when corrections were made, format drift was fixed, new patterns emerged, or the user explicitly asks to "harvest learnings" or "capture session intelligence". Transforms one-time fixes into permanent organizational knowledge by implementing updates across multiple files.
ai-collaborate-teaching
by 92Bilal26Design co-learning experiences using the Three Roles Framework (AI as Teacher/Student/Co-Worker). Use when teaching AI-driven development workflows, spec-first collaboration, or balancing AI assistance with foundational learning. NOT for curriculum without AI integration.
assessment-builder
by 92Bilal26Creates assessments with varied question types (MCQ, code-completion, debugging, projects) aligned to learning objectives with meaningful distractors based on common misconceptions. Activate when educators design quizzes, exams, or tests measuring understanding; need questions at appropriate cognitive levels (Bloom's taxonomy); want balanced cognitive distribution (60%+ non-recall); or require rubrics for open-ended questions. Generates MCQs with diagnostic distractors, code-writing prompts, debugging challenges, and project-based assessments targeting deep understanding.
book-scaffolding
by 92Bilal26Plan, structure, and scaffold large educational books using cognitive load management, just-in-time specification, and pedagogical best practices. Use this skill when planning multi-part, multi-chapter educational works that require narrative continuity, progressive complexity, and hands-on exercises. This skill helps create cohesive learning journeys that balance foundational scaffolding with advanced independence. Activate when tasks involve structuring books, managing cognitive load across chapters, defining part-level narratives, or coordinating multi-phase content development workflows.
canonical-format-checker
by 92Bilal26This skill should be used when content teaches patterns (skills, subagents, ADRs, PHRs, specifications) that have canonical sources elsewhere. Prevents format drift by ensuring content references and follows the authoritative format from canonical sources. Use before implementing lessons that teach platform patterns, or when reviewing content for format consistency.
chatkit-botbuilder
by 92Bilal26Guide for creating production-grade ChatKit chatbots that integrate OpenAI Agents SDK with MCP tools and custom backends. Use when building AI-powered chatbots with specialized capabilities, real-time task execution, and user isolation for any application.
code-validation-sandbox
by 92Bilal26Validate code examples across the 4-Layer Teaching Method with intelligent strategy selection. Use when validating Python/Node/Rust code in book chapters. NOT for production deployment testing.
content-evaluation-framework
by 92Bilal26This skill should be used when evaluating the quality of book chapters, lessons, or educational content. It provides a systematic 6-category rubric with weighted scoring (Technical Accuracy 30%, Pedagogical Effectiveness 25%, Writing Quality 20%, Structure & Organization 15%, AI-First Teaching 10%, Constitution Compliance Pass/Fail) and multi-tier assessment (Excellent/Good/Needs Work/Insufficient). Use this during iterative drafting, after content completion, on-demand review requests, or before validation phases.
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.