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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emergency-card
by ComeOnOliver生成紧急情况下快速访问的医疗信息摘要卡片。当用户需要旅行、就诊准备、紧急情况或询问"紧急信息"、"医疗卡片"、"急救信息"时使用此技能。提取关键信息（过敏、用药、急症、植入物），支持多格式输出（JSON、文本、二维码），用于急救或快速就医。
health-insights
by glowingkittyGet health insights and find the right doctor.
prepare-doctor-appointment
by glowingkittyPrepare what to discuss at your next visit.
epic-note
by realactivityDrafts a concise patient-portal message the user can copy-paste to a clinician via Epic MyChart, Oracle Health HealtheLife, or similar. Personal data is referenced from an external profile file, never embedded. USE FOR: symptom reports, refill requests, lab/result follow-ups, visit follow-ups, asking a single clear question. DO NOT USE FOR: clinical notes, SOAP/discharge documentation, insurance or billing letters, or answering the patient's medical question.
medicare-part-d-eligibility-assessment
by gabrielmoreiraConduct an interactive, one-by-one question-and-answer session to determine a user's eligibility for Medicare Part D enrollment without penalty and calculate the coverage start date.
organ-nutrient-food-mapping-generator
by gabrielmoreiraGenerates exhaustive lists mapping human body organs to specific nutrients (minerals or vitamins) and their corresponding food sources.
appointments
by The-AI-AllianceUse this skill for managing patient appointments - creating, canceling, confirming, changing, and listing appointments.
health-blood-pressure-log
by khalilbenazSuit la tension artérielle et/ou la glycémie dans le temps avec tableaux et tendances. À utiliser quand l'utilisateur note des mesures de tension ou de glycémie et veut les organiser. Se déclenche aussi avec "ma tension", "ma glycémie", "tensio", "12/8", "j'ai pris ma tension", "taux de sucre", ou toute mention de mesures de constantes vitales régulières.
allergy-reaction-log
by khalilbenazSuit les réactions allergiques ou suspectées, identifie les déclencheurs et prépare un dossier pour l'allergologue. À utiliser quand l'utilisateur décrit des réactions cutanées, respiratoires ou digestives suspectes. Se déclenche aussi avec "réaction allergique", "urticaire", "j'ai gonflé", "allergie", "éternuements", "yeux qui piquent", "eczéma", ou toute mention de réaction inexpliquée après exposition.
medication-schedule
by khalilbenazOrganise les prises de médicaments et compléments dans une journée sans remplacer l'ordonnance. À utiliser quand l'utilisateur veut un planning horaire simple ou éviter les oublis. Se déclenche aussi avec "planning médicaments", "quand prendre mes cachets", "organiser mes prises", "rappel médicament", "emploi du temps traitement".
health-diet-trigger-journal
by khalilbenazMet en relation alimentation et symptômes pour identifier des déclencheurs possibles. À utiliser quand l'utilisateur suspecte qu'un aliment ou un repas aggrave ses symptômes. Se déclenche aussi avec "après avoir mangé", "quand je mange du…", "intolérance", "ballonnements après repas", "diarrhée après…", "journal alimentaire", ou toute mention de lien entre nourriture et gêne.
counseling-patient-education
by lev-osStructures medication counseling with key points, administration instructions, and adherence strategies. Use when counseling patients on medications, creating medication guides, or preparing patient education materials.
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.