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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exercise-biomechanics
by revfactory운동 동작의 생체역학 분석과 안전한 수행 가이드. 'exercise-guide' 에이전트가 운동 동작을 설명하고 대체 운동을 제시할 때 이 스킬의 근육 활성화 데이터, 관절 부하 분석, 부상 예방 가이드를 반드시 활용해야 한다. '운동 폼 가이드', '근육 활성화', '부상 예방', '대체 운동' 등에 사용한다. 단, 프로그램 설계나 영양 전략은 이 스킬의 범위가 아니다.
fitness-program
by revfactoryA full pipeline where an agent team collaborates to generate everything from goal-based fitness program design to progress tracking templates. Use this skill for requests related to fitness and training programs such as 'create a workout program', 'gym routine', 'strength program', 'diet workout', 'home training', 'weekly workout schedule', 'bulk-up program', 'marathon training', 'workout routine recommendation', 'PPL program', 'bodyweight routine', etc. If an existing program is provided, analysis or improvement is supported. However, rehabilitation program prescription (physical therapist work), performance-enhancing drug consultation, and real-time personal training are outside the scope of this skill.
training-log-analyzer
by MicrockTrack workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
body-composition-analyzer
by vitaclawAnalyzes body composition metrics including body fat percentage, muscle mass, visceral fat, and BMI. Tracks trends over time and provides training and nutrition recommendations based on body composition goals. Use when the user logs body measurements, DEXA/BIA results, or asks about body composition.
breathing-exercise-guide
by vitaclawProvides structured breathing exercise programs for stress relief, sleep improvement, focus enhancement, and anxiety management. Includes step-by-step guided instructions for various techniques. Use when the user wants breathing exercises, asks about breathwork, or needs quick relaxation techniques.
calorie-fitness-manager
by vitaclawManages daily calorie balance and fitness tracking by coordinating BMR/TDEE calculation, nutrition analysis, food lookup, exercise stats, trend analysis, and SMART goal tracking. Use when the user wants to track calories, manage weight loss or gain, or optimize their fitness routine.
circadian-rhythm-optimizer
by vitaclawAnalyzes circadian rhythm patterns, assesses chronotype (morningness-eveningness), provides light exposure protocols, optimizes meal/exercise/sleep timing windows, and supports jet lag recovery and shift work adaptation. Use when the user asks about their body clock, optimal daily timing, light exposure, jet lag, or shift schedules.
google-fit-digest
by vitaclawAnalyzes Google Fit exported data including steps, heart rate, sleep, and activity metrics. Generates health digests and trend reports from Google Fit JSON/CSV exports. Use when the user provides Google Fit data or asks about their Google Fit health metrics.
sleep-analyzer
by vitaclawAnalyzes sleep data to compute efficiency, quality score (0-100), and stage distribution. Detects patterns like irregular schedules, chronic short sleep, and weekend oversleep. Use when the user provides sleep records, imports Apple Health data, or asks about sleep quality.
sleep-optimizer
by vitaclawGenerates prioritized, personalized sleep improvement recommendations based on sleep metrics, caffeine data, screen time, and exercise timing. Use after sleep-analyzer identifies issues or when the user asks how to improve sleep.
weekly-health-digest
by vitaclawAggregates the past 7 days of health data from health-memory into a narrative weekly report with a composite health score (0-100), per-domain summaries, cross-domain correlations, and actionable next-week suggestions. Use at the end of each week or when the user asks for a health summary.
fit
by dvcrnYour personal fitness operating system. Not a workout plan. A complete system that figures out what your body actually needs, builds training around your real life, tracks what is working, and adapts when life gets in the way. Trigger for any fitness goal: losing fat, building muscle, running faster, getting stronger, recovering from injury, or simply moving better than you did last year.
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.