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...
eldercare-exercise
by nclamvnHướng dẫn bài tập nhẹ cho người cao tuổi nằm giường hoặc ngồi. TTS đọc từng bước chậm rãi, đếm giữ, nghỉ giữa các bài. Trigger: Gia đình nhắn "tập thể dục cho bà" hoặc cron hàng ngày. Tất cả bài tập AN TOÀN cho người 90+ nằm giường. Disabled by default — gia đình bật khi sẵn sàng.
cardiac-care-unit-nursing-support
by gabrielmoreiraProvides clinical support, patient monitoring, emergency response, and multidisciplinary coordination for acute cardiac conditions, procedures, and rehabilitation in a Cardiac Care Unit setting.
sleep-journal
by khalilbenazSuit la qualité du sommeil, les habitudes nocturnes et les facteurs perturbateurs pour identifier des schémas. À utiliser quand l'utilisateur se plaint d'insomnie, de fatigue au réveil, de réveils nocturnes ou de troubles du sommeil. Se déclenche aussi avec "je dors mal", "insomnie", "je me réveille la nuit", "je suis fatigué le matin", "sommeil perturbé", "cauchemars", ou toute mention de difficulté liée au sommeil.
symptom-tracker
by khalilbenazSuit les symptômes de santé dans le temps et identifie les déclencheurs possibles. À utiliser quand l'utilisateur décrit un problème, une évolution, une douleur, une diarrhée, une fatigue, un inconfort digestif ou des épisodes répétés. Se déclenche aussi avec "j'ai mal", "je me sens", "ça revient souvent", "journal de santé", "préparer mon rendez-vous médecin", ou toute description de gêne physique ou mentale récurrente.
health-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.
conducting-discharge-planning-nursing
by lev-osCoordinates nursing discharge planning with medication teaching, follow-up scheduling, and resource coordination. Use when planning discharge, coordinating post-discharge care, or documenting discharge teaching.
documenting-nursing-notes
by lev-osStructures nursing progress notes with SBAR communication and clinical narrative documentation. Use when writing nursing notes, documenting patient updates, or creating SBAR communications.
managing-central-line-care
by lev-osStructures central line maintenance with bundle compliance and infection prevention documentation. Use when managing central lines, documenting line care, or tracking bundle compliance.
managing-iv-therapy
by lev-osGuides IV access assessment, site management, and complication monitoring with documentation. Use when managing IV therapy, assessing IV sites, or documenting infusion monitoring.
managing-nursing-quality-metrics
by lev-osTracks nursing quality indicators (NDNQI, HCAHPS) with performance improvement documentation. Use when monitoring nursing quality, tracking NDNQI metrics, or managing quality improvement.
managing-perioperative-nursing
by lev-osStructures perioperative nursing documentation with pre/intra/post-operative assessments and counts. Use when documenting OR nursing care, performing surgical counts, or managing perioperative documentation.
managing-skin-integrity
by lev-osConducts Braden scale assessment with pressure injury prevention interventions and documentation. Use when assessing skin integrity, calculating Braden scores, or implementing pressure injury prevention.
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.