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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reference-generator
by dmccrearyThis skill generates curated, high-quality reference lists for textbooks with 10 references per chapter. References prioritize Wikipedia for reliability, include detailed relevance descriptions, and are stored in separate references.md files for token efficiency. Use this skill when working with intelligent textbooks that need academic references.
textbook-to-presentation-generator
by dmccrearyGenerate a compelling PowerPoint lecture presentation from an intelligent textbook (MkDocs project with chapters, learning graph, and course description). The presentation embodies McLuhan's "the medium IS the message" — every slide exemplifies the principle it teaches. Uses pptxgenjs to create .pptx files with speaker notes, visual design, and 4-act storytelling structure. Use this skill whenever the user wants to create a presentation, lecture deck, slide deck, or PowerPoint from a textbook, course, or educational content. Also use when converting book content into presentation format, or when the user says "create slides", "make a deck", "build a presentation", "lecture slides", or "presentation from the textbook".
moving-rainbow
by dmccrearyGenerate MicroPython programs for the Moving Rainbow LED strip educational project using Raspberry Pi Pico with NeoPixel strips and button controls.
chapter-image-enhancer
by dmccrearyAdd freely-licensed maps, photos, and diagrams to textbook chapters by finding images from Wikimedia Commons and US government sources, downloading and optimizing them, and inserting them with proper captions and attribution. Use this skill whenever a user wants to add visuals, images, photos, or maps to a chapter, make a chapter more visual, or says a chapter needs pictures. Also trigger when the user mentions "chapter images", "add photos", "find images for", "visual enhancement", or "image credits".
glossary-generator
by dmccrearyThis skill automatically generates a comprehensive glossary of terms from a learning graph's concept list, ensuring each definition is precise, concise, distinct, non-circular, and free of business rules. Use this skill when creating a glossary for an intelligent textbook after the learning graph concept list has been finalized.
microsim-utils
by dmccrearyUtility tools for MicroSim management including quality validation, screenshot capture, icon management, index page generation, and iframe height synchronization. Routes to the appropriate utility based on the task needed.
book-chapter-generator
by dmccrearyThis skill generates a structured chapter outline for intelligent textbooks by analyzing course descriptions, learning graphs, and concept dependencies. Use this skill after the learning graph has been created and before generating chapter content, to design an optimal chapter structure that respects concept dependencies and distributes content evenly across all of the chapter in a book.
chapter-content-generator
by dmccrearyThis skill generates comprehensive chapter content for intelligent textbooks after the book-chapter-generator skill has created the chapter structure. Use this skill when a chapter index.md file exists with title, summary, and concept list, and detailed educational content needs to be generated at the appropriate reading level with rich non-text elements including diagrams, infographics, and MicroSims. (project, gitignored)
concept-classifier
by dmccrearyCreate an interactive classification quiz MicroSim using p5.js where students read scenarios and classify them into the correct category from multiple choice options. Uses a data.json file for easy question editing. Ideal for teaching students to recognize patterns, identify types, or categorize examples across any subject domain.
course-description-analyzer
by dmccrearyThis skill analyzes or creates course descriptions for intelligent textbooks by checking for completeness of required elements (title, audience, prerequisites, topics, Bloom's Taxonomy outcomes) and providing quality scores with improvement suggestions. Use this skill when working with course descriptions in /docs/course-description.md that need validation or creation for learning graph generation.
faq-generator
by dmccrearyThis skill generates a comprehensive set of Frequently Asked Questions (FAQs) from the course description, course content, learning graphs, concept lists, MicroSims, and glossary terms to help students understand common questions and prepare content for chatbot integration. Use this skill after course description, learning graph, glossary, and at least 30% of chapter content exist.
learning-graph-generator
by dmccrearyGenerates a comprehensive learning graph from a course description, including 200 concepts with dependencies, taxonomy categorization, and quality validation reports. Use this when the user wants to create a structured knowledge graph for educational content.
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.