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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fhir-hl7-validator
by 1Mangesh1This skill should be used when the user asks to "validate FHIR resources", "check HL7 messages", "validate healthcare data format", "parse FHIR", "HL7 v2 messages", "FHIR R5 validation", "CDA documents", "healthcare data interchange", "FHIR resource schema", "HL7 specifications", or mentions FHIR validation, HL7 message parsing, CDA validation, healthcare data format compliance, or Fast Healthcare Interoperability Resources standards.
healthcare-audit-logger
by 1Mangesh1This skill should be used when the user asks to "generate audit logs", "create HIPAA audit trail", "log healthcare events", "configure audit logging", "track PHI access", "maintain compliance logs", "audit log format", "healthcare event logging", "access control logging", "authentication logging", "HIPAA logging requirements", or mentions HIPAA audit trails, healthcare event logging, compliance logging, PHI access tracking, authentication auditing, or §164.312(b) logging requirements.
hipaa-guardian
by 1Mangesh1This skill should be used when the user asks to "scan for PHI", "detect PII", "HIPAA compliance check", "audit for protected health information", "find sensitive healthcare data", "generate HIPAA audit report", "check code for PHI leakage", "scan logs for PHI", "check authentication on PHI endpoints", "scan FHIR resources", "check HL7 messages", or mentions PHI detection, HIPAA compliance, healthcare data privacy, medical record security, logging PHI violations, authentication checks for health data, or healthcare data formats (FHIR, HL7, CDA).
pnpm
by 1Mangesh1pnpm package manager for fast, disk-efficient dependency management and workspaces. Use when user mentions "pnpm", "pnpm install", "pnpm workspace", "pnpm-workspace.yaml", "pnpm add", "pnpm store", "pnpm dlx", "content-addressable store", or managing Node.js packages with pnpm.
ollama
by 1Mangesh1Ollama for running local LLMs — model management, API usage, and integration patterns. Use when user mentions "ollama", "local LLM", "run llama locally", "local AI", "ollama run", "ollama pull", "self-hosted model", "offline AI", "local inference", or running language models on their own machine.
k6-load-testing
by 1Mangesh1k6 for load testing, performance testing, and API stress testing. Use when user mentions "k6", "load testing", "stress testing", "performance testing", "API load test", "concurrent users", "ramp up", "throughput testing", "soak test", "spike test", or testing how an application handles traffic.
supabase
by 1Mangesh1Supabase for database, auth, storage, and realtime features. Use when user mentions "supabase", "supabase auth", "supabase storage", "supabase realtime", "supabase edge functions", "postgres with supabase", "row level security", "RLS", "supabase client", or building apps with Supabase as the backend.
api-design
by 1Mangesh1REST API design patterns, structure, and best practices. Use when user asks to "design a REST API", "create API endpoints", "write OpenAPI spec", "design API routes", "add pagination to API", "version an API", "create API schema", "design webhook endpoints", "structure API responses", "implement HATEOAS", "design API errors", "API versioning", "API deprecation", "rate limiting design", or mentions REST API design, endpoint naming, HTTP methods, status codes, API best practices, request/response design, or API documentation.
caching-strategies
by 1Mangesh1Caching strategies and implementation patterns. Use when user asks to "add caching", "cache API responses", "set up CDN caching", "configure HTTP caching", "implement memoization", "cache database queries", "set cache headers", "invalidate cache", "design cache layer", "reduce API latency", "cache warming", "cache busting", "distributed caching", "Redis caching", "edge caching", or mentions caching strategies, cache invalidation, TTL, cache-aside, write-through, write-behind, CDN, browser caching, or memoization.
ci-cd-pipelines
by 1Mangesh1CI/CD pipeline design, setup, and optimization. Use when user asks to "set up CI/CD", "create a pipeline", "configure Jenkins", "set up GitLab CI", "create CircleCI config", "automate deployments", "create build pipeline", "set up continuous deployment", "configure pipeline stages", "add pipeline caching", "pipeline security", "secret management", "artifact management", "GitHub Actions workflow", "deployment strategies", "canary deployment", or mentions CI/CD pipelines, continuous integration, continuous deployment, build automation, deployment pipelines, or pipeline optimization.
database-indexing
by 1Mangesh1Database indexing internals, index type selection, query plan analysis, and write-overhead tradeoffs across PostgreSQL, MySQL, and MongoDB. Use when user asks to "optimize queries", "create indexes", "fix slow queries", "read EXPLAIN output", "reduce query time", "index strategy", "database performance", "composite index", "covering index", "partial index", "index bloat", "unused indexes", or needs help diagnosing and resolving database performance problems.
dependency-audit
by 1Mangesh1Dependency auditing, updating, and vulnerability management for npm, pip, and other package managers. Use when user asks to "audit dependencies", "update packages", "fix vulnerabilities", "check outdated", "npm audit", "pip audit", "upgrade dependencies safely", or any dependency management tasks.
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.