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...
api-security-review
by thomast1906Reviews Azure API Management configurations for security vulnerabilities, OWASP API Security Top 10 compliance, VNet Internal mode validation, Private Link verification, and Azure Security Benchmark alignment. Use when performing security audits, pre-deployment validation, or compliance reviews.
apim-policy-authoring
by thomast1906Creates production-ready Azure API Management policy XML for authentication (OAuth 2.0, JWT validation, subscription keys), rate limiting, CORS configuration, error handling, and API transformations. Use when implementing API security, access control, or request/response processing logic.
apiops-deployment
by thomast1906Guides deployment of Azure API Management infrastructure using Infrastructure as Code (Bicep/Terraform), CI/CD pipelines (GitHub Actions/Azure DevOps), and APIOps workflows. Use when deploying APIM, creating pipelines, or implementing dev→test→prod promotion strategies.
apm-package-author
by thomast1906Create, maintain, and troubleshoot APM (Agent Package Manager) manifests for distributing GitHub Copilot skills, agents, and MCP servers. USE FOR: creating apm.yml root manifests; creating packages/* sub-manifests; bundling MCP server config into a package; installing packages from a GitHub repo; troubleshooting APM install errors (missing .vscode/mcp.json, Codex CLI warnings, cached installs). DO NOT USE FOR: general GitHub Copilot customization questions; creating SKILL.md files (use skill-creator); writing MCP server code.
architecture-design
by thomast1906Design Azure cloud architectures from requirements and generate High-Level Design (HLD) documentation with service selection, patterns, cost estimates, and WAF alignment. Use this when asked to design or architect Azure solutions.
azure-apim-architecture
by thomast1906Analyzes and explains Azure API Management architecture decisions for enterprise API marketplace implementations using VNet Internal mode, Front Door, hybrid authentication, and multi-environment strategies. Use when discussing APIM component selection, network topology, cost optimization, or comparing alternatives like workspaces vs instances, VNet Internal vs External mode, or Front Door vs Application Gateway.
azure-drawio-mcp-diagramming
by thomast1906Create and edit architecture diagrams using Draw.io MCP (`drawio/create_diagram`) with reliable Azure icon rendering guidance and troubleshooting. compatibility Requires Python 3 and internet access to refresh the icon catalog (periodic, not per-run).
azure-pricing
by thomast1906Look up real-time Azure retail pricing for any service, SKU, or region using the Azure MCP pricing tool. Estimate deployment costs from Bicep, ARM, or Terraform templates, compare pricing across regions, price types (Consumption, Reservation, DevTest), and surface savings plan options. Use when asked about Azure costs, SKU prices, region comparisons, or template cost estimates.
cost-optimization
by thomast1906Analyze Azure architectures for cost optimization opportunities, provide savings recommendations, and calculate ROI for improvements.
drawio-mcp-diagramming
by thomast1906Create and edit architecture diagrams using Draw.io MCP (`drawio/create_diagram`) with reliable Azure and AWS icon rendering guidance and troubleshooting. Supports Azure2 and AWS4 icon libraries. Requires Python 3 and internet access to refresh icon catalogs (periodic, not per-run).
excalidraw-mcp-diagramming
by thomast1906Create and edit diagrams on a live Excalidraw canvas using the Excalidraw MCP server. Use when asked to draw, diagram, sketch, or visualise architectures, workflows, data flows, system designs, flowcharts, mind maps, or sequence diagrams. Trigger phrases include "create an excalidraw", "draw me a diagram", "make a flowchart", "visualise the system", "diagram this architecture", "export to PNG/SVG". Can export to PNG, SVG, .excalidraw file, or a shareable URL. Do NOT use for Draw.io or diagrams.net output (use drawio-mcp-diagramming instead).
gh-aw-operations
by thomast1906Comprehensive skills for creating, compiling, debugging, and managing GitHub Agentic Workflows (gh-aw) with best practices and common patterns
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.