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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health-educate
by XiaoLuoLYGExplain practical health advice in everyday language.
neuroskill-bci
by raphaelmansuyConnect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or OpenBCI) and the NeuroSkill desktop app running locally.
breath-and-meditation-foundations
by TibsfoxFoundations of breath practice and seated meditation across traditions — diaphragmatic mechanics, pranayama families (ujjayi, nadi shodhana, bhramari, kapalabhati, bhastrika, sitali, sheetkari), buffer-safe breath-retention posture, shikantaza and koan traditions in Sōtō and Rinzai Zen, secular clinical mindfulness as MBSR translates it, and engaged-mindfulness pedagogy as Plum Village teaches it. Use when setting up a breath practice, designing a meditation sit, debugging a stalled practice, or deciding whether a given technique is safe for a given person.
occupational-health-analyzer-v2
by diegosouzapw职业健康分析技能 workflow skill. Use this skill when the user needs 分析职业健康数据、识别工作相关健康风险、评估职业健康状况、提供个性化职业健康建议。支持与睡眠、运动、心理健康等其他健康数据的关联分析。 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
claude-ally-health
by diegosouzapwClaude Ally Health workflow skill. Use this skill when the user needs A health assistant skill for medical information analysis, symptom tracking, and wellness guidance and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
sexual-health-analyzer-v2
by diegosouzapw性健康分析技能 workflow skill. Use this skill when the user needs Sexual Health Analyzer and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
sexual-health-analyzer
by Anhvu1107ALWAYS use this when the user mentions Sexual Health Analyzer, asks to build, debug, review, document, automate, test, configure, migrate, or make decisions in this domain, or the task clearly depends on Sexual Health Analyzer; scope: Sexual Health Analyzer work. Apply the bundled workflow, references, scripts, Senior Master standard, and Codex strict review gate before final output.
health
by priyanshuchaudhary53Help someone think about and improve their physical and mental health. Use when someone wants to build better health habits, is struggling with energy, wants to understand Naval's approach to diet and exercise, or is trying to make health a real priority.
mental-wellness
by aiunlocked1412Self-help mental wellness coach — daily journaling prompts + gratitude practice + emotional regulation tools + thought reframing สำหรับคนทั่วไป (ไม่ใช่ clinical therapy)
prevention-strategy
by kangarooking用户想预防某个潜在风险或问题时; 当系统出现早期预警信号但尚未爆发时; 当已出现问题需要防止恶化和扩散时; 当需要建立长期防护机制时。 不适用于: 紧急危机处理(系统正在崩溃, 需要急救而非预防); 纯历史回顾 (分析过去发生了什么但不涉及未来预防)。
holistic-self-care-and-breathwork-content-writing
by gabrielmoreiraGenerates creative, professional, and captivating content for self-help books or guides, focusing on holistic well-being, breathwork, and mindfulness by integrating ancient wisdom with modern science.
law-enforcement-wellness-book-writer
by gabrielmoreiraGenerates structured book sections (approx. 500 words) on mental wellness, resilience, and stress management, tailored for law enforcement officers with relevant terminology and examples.
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.