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...
running
by GoogleCloudPlatformUse when a runner agent needs to manage athletic performance during a race tick: accelerating, braking, or reading current vitals (speed, energy, hydration, exhaustion). Triggered every tick the runner is active.
cardiovascular-fitness
by TibsfoxCardiovascular fitness assessment and prescription for physical education. Covers VO2max, the Cooper 12-minute run, target heart rate zones, the FITT framework (Frequency, Intensity, Time, Type), aerobic versus anaerobic energy systems, and progression principles for building aerobic capacity safely at every age. Use when designing fitness units, assessing baseline cardiovascular health, prescribing exercise, explaining why aerobic work matters, or translating sports medicine evidence into classroom practice.
coaching-and-teaching
by TibsfoxCoaching as teaching — John Wooden's Pyramid of Success, practice design, feedback quality, instructional economy, and the craft of deliberate skill development. Covers the difference between knowing the game and teaching it, Wooden's actual practice methods as documented by Gallimore and Tharp, skill progression through part-whole teaching, the four-to-one positive feedback discipline, and the habits that distinguish effective coaches from merely knowledgeable ones. Use when designing practices, improving instruction, mentoring young coaches, or framing sport leadership as an educational activity.
inclusive-physical-education
by TibsfoxInclusive physical education for gender, ability, and developmental variation. Covers the history of women in sport from Berenson's women's basketball rules forward, adapted PE for disability and chronic illness, universal design for learning in PE, gender-equitable participation, and the ethical obligations of a PE teacher to serve every learner in the room. Use when adapting lessons for disability, designing co-educational units, addressing participation gaps, or teaching the history of inclusion as part of the PE curriculum.
movement-fundamentals
by TibsfoxFundamental movement skills and motor learning for physical education. Covers the three movement families (locomotor, non-locomotor, manipulative), the stage theory of motor learning (cognitive, associative, autonomous), developmental coordination milestones, and the teaching progression from gross to fine motor control. Use when designing introductory PE lessons, assessing motor competence, diagnosing movement gaps in older learners, or building the movement base on which sport-specific skills later stand.
sport-education-pedagogy
by TibsfoxSport Education model and physical education pedagogy. Covers Siedentop's Sport Education model (seasons, affiliation, formal competition, record-keeping, festivity, culminating event), unit and lesson design for PE, grouping strategies, assessment frameworks, and the shift from "teaching activities" to "teaching sport as an authentic practice." Use when designing PE unit plans, transforming a traditional activity-of-the-week approach into a durable educational program, or aligning assessment with educational intent.
strength-and-conditioning
by TibsfoxStrength, power, and conditioning principles for physical education. Covers the seven classical strength adaptations (hypertrophy, maximal strength, power, endurance, speed, agility, mobility), resistance training modalities, periodization models, age-appropriate progression, and injury prevention. Use when designing resistance units, prescribing off-season conditioning, adapting training for adolescent development, or integrating strength work into sport-specific preparation.
challenge-course
by kurskuChallenge Course — Skill especializada para design, implementação e avaliação de programas de desafio e aventura, focando em desenvolvimento de equipes e liderança.
football
by clawicAnalyze football and soccer matches, squads, players, and training plans with tactical frameworks, scouting grids, and session blueprints.
langping-skill
by lucian55郎平（排球 / 教练）认知与表达框架（压缩蒸馏）：集体主义与个人英雄平衡、暂停与换人话术 触发：女排、铁榔头 等。非煽动对立
outward-bound-trainer
by HaibarakikuExpert Outward Bound Trainer with 15+ years of experience in adventure-based learning, leadership development, and team building
coaching-philosophy
by WinbdaDevelop coaching philosophies with values and methods. TRIGGERS - Use when user needs help with coaching-philosophy related tasks.
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.