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...
understanding-check
by harvard-lilHelps law students check their understanding of course material, test whether they grasp key concepts, identify gaps in their knowledge, or review what they've learned so far in a class. Use when the student wants to verify comprehension, diagnose weak spots, or assess readiness before an exam or the next class.
professor-meta
by harvard-lilAlways-on assistant for law professors and legal educators. Covers course design, syllabus creation, assessment generation, pedagogy, grading, learning objectives, and all aspects of teaching law. Use when the user is a law professor, legal educator, or anyone designing or improving a law school course, seminar, or workshop.
syllabus-evidence-based
by harvard-lilCreates a modern, evidence-based law school course syllabus from provided content (uploaded PDFs, book table of contents images, pasted text). Uses spiral structure, spaced practice, interleaving, scaffolded complexity, and backward design drawn from learning science research. Use when the user says "I want a syllabus that revisits key concepts throughout the semester instead of covering them once," "build a course plan that uses spaced practice and cumulative assessments," "create a syllabus based on learning science research, not just the casebook order," "design a scaffolded syllabus where students get more independence as the semester progresses," or "I want something more intentional than just following the book chapter by chapter."
syllabus-traditional
by harvard-lilCreates a traditional Socratic law school course syllabus from provided content (uploaded PDFs, book table of contents images, pasted text). Uses linear, block-based doctrinal sequencing with canonical casebook ordering. Use when the user says "build a syllabus from this casebook table of contents," "create a syllabus that follows the book chapter by chapter," "I need a standard 1L Contracts syllabus with case assignments," "map out 28 class sessions covering this Torts material," or "generate a Socratic method course plan from these readings."
skill-reviewer
by harvard-lilReviews and evaluates an existing SKILL.md for quality, pedagogical soundness, persona compliance, and agent-readiness. Triggers when the user wants feedback on a skill they've written, wants to improve an existing skill, or wants to check whether a skill meets the Legal Ed Skills Hub standards.
exam-answer-eval
by harvard-lilProvides feedback on practice exam answers, sample essays, or issue-spotter responses. Use when a law student wants to review a practice exam answer, get feedback on an essay, improve exam performance, or prepare for future exams.
socratic-tutor
by harvard-lilPrepares law students for class by quizzing them Socratically on assigned readings, cases, or topics. Use when the student wants to practice articulating legal reasoning under pressure, prepare for cold calls, or engage in Socratic dialogue on cases and doctrines.
student-meta
by harvard-lilAlways-on assistant for law students. Covers studying, class prep, exam prep, outlining, understanding cases, legal writing, self-assessment, and any law-student task. Use when the user is a law student working on coursework, preparing for class, studying for exams, or developing legal analysis skills.
skill-creator
by harvard-lilHelps someone create a new pedagogical AI skill from scratch. Triggers when the user wants to write a SKILL.md, build a new skill for the Legal Ed Skills Hub, design an AI-assisted learning experience, or turn a teaching approach into an agent skill. No technical expertise required.
skill-developer-meta
by harvard-lilAlways-on assistant for creating, reviewing, and testing pedagogical AI skills for the Legal Ed Skills Hub. Triggers on writing new skills, reviewing skill quality, evaluating skill pedagogy, testing skills against rubrics, defining evaluation criteria, or any task related to building and improving agent skills for legal education.
feedback-coach
by harvard-lilChecks an instructor's draft feedback on a student response against the assignment's grading rubric and rewrites it to be constructive, specific, and accessible to a neurodiverse audience. Use when the user says "help me rewrite this feedback on a student's exam answer," "does my feedback match the rubric," "make this comment more constructive and clearer," "check whether my feedback is autism-friendly or hard to read," or "review the feedback I drafted for this student before I send it."
syllabus-traditional
by harvard-lilCreates a traditional Socratic law school course syllabus from provided content (uploaded PDFs, book table of contents images, pasted text). Uses linear, block-based doctrinal sequencing with canonical casebook ordering. Use when the user wants a conventional law school syllabus, a Socratic method syllabus, or a traditional course plan.
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.