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...
muscle-gain
by sundial-orgTrack muscle building with weight progression, protein tracking, and strength milestones
somatic-movement-pilates-feldenkrais
by TibsfoxSomatic movement and whole-body retraining as Joseph Pilates designed it in the Contrology method and as Moshé Feldenkrais designed it in Awareness Through Movement and Functional Integration, with enough cross-reference to the broader somatics landscape (Alexander Technique, Hanna Somatics, Body-Mind Centering) that an agent can place a user into the right method. Covers the Pilates reformer and mat system, the six Pilates principles, the Feldenkrais ATM lesson structure, the nervous-system learning frame that distinguishes somatics from exercise, and the safety posture that matters for rehab populations. Use for queries about core training, rehab-adjacent movement, chronic pain patterns, and learning-based movement re-education.
rehabilitation-analyzer
by diegosouzapw康复训练分析技能 workflow skill. Use this skill when the user needs 分析康复训练数据、识别康复模式、评估康复进展，并提供个性化康复建议 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
rehabilitation-analyzer
by Anhvu1107ALWAYS use this when the user mentions Rehabilitation Analyzer, asks to build, debug, review, document, automate, test, configure, migrate, or make decisions in this domain, or the task clearly depends on Rehabilitation Analyzer; scope: 分析康复训练数据、识别康复模式、评估康复进展，并提供个性化康复建议. Apply the bundled workflow, references, scripts, Senior Master standard, and Codex strict review gate before final output.
krumpphysio
by dvcrnTeaches OpenClaw agents to act as a Krump-inspired physiotherapy coach. Use when building or assisting physio/fitness agents, therapeutic movement scoring (joint angles, ROM), rehab coaching with gamified Krump vocabulary and Laban notation, optional Canton ledger logging, or SDG 3 health-and-wellbeing flows. Grounds advice in authentic krump adapted for physiotherapy.
neuroskill-protocols-routines
by NeuroSkill-comMorning routines, workout/gym, hydration, bathroom and movement break protocols — daily rituals and exercise-adjacent interventions.
exercise-form
by bytesagainExercise form guide with warmup routines and workout plans. Use when planning workouts.
post-surgery-tracker
by khalilbenazSuit la récupération après une opération chirurgicale avec suivi des symptômes, douleurs, cicatrisation et rendez-vous. À utiliser quand l'utilisateur mentionne une opération récente ou une convalescence. Se déclenche aussi avec "après mon opération", "post-opératoire", "convalescence", "ma cicatrice", "j'ai été opéré", ou toute mention de récupération chirurgicale.
insights
by compound-life-aiDiscover patterns in health data, answer questions about correlations, and guide structured self-experiments with observation, hypothesis, check-ins, analysis, and next-step recommendations.
creating-rehabilitation-treatment-plans
by lev-osDevelops rehabilitation treatment plans with goals, interventions, and measurable outcome milestones. Use when creating rehab plans, setting therapy goals, or planning intervention progressions.
managing-cardiac-rehabilitation
by lev-osStructures cardiac rehab prescriptions with exercise parameters and risk stratification. Use when prescribing cardiac rehab, setting exercise targets, or monitoring rehab progress.
managing-cardiac-rehabilitation-therapy
by lev-osStructures cardiac rehab exercise prescription with monitoring parameters and progression criteria. Use when prescribing cardiac rehab exercise, monitoring exercise response, or documenting rehab progression.
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.