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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extract-facade-from-base-lib
by ruby-gitMigrates a public method from Git::Base or Git::Lib to a Git::Repository facade method (under lib/git/repository/) as part of the v5.0.0 architectural redesign. Use when extracting a specific public method during the Strangler Fig migration of Git::Base / Git::Lib into Git::Repository.
extract-command-from-lib
by ruby-gitMigrates a direct #command call in Git::Lib to a Git::Commands::* class as part of the architectural redesign. Use when extracting a specific command during the Strangler Fig migration.
yard-documentation
by ruby-gitGeneral YARD documentation rules and workflow for all Ruby source code. Use when writing or reviewing YARD doc comments, generating missing docs, updating examples, fixing doc errors, or checking documentation coverage.
review-cross-command-consistency
by ruby-gitCompares sibling command classes for consistent structure, documentation, testing, and exit-status conventions under the Base architecture. Use for cross-command audits.
pr-readiness-review
by ruby-gitPerforms a comprehensive pre-PR readiness review covering tests, code quality, security, and commit conventions. Use at the end of implementation before submitting a pull request.
project-context
by ruby-gitReference guide for ruby-git architecture, coding standards, design philosophy, key technical details, and compatibility requirements. Use when answering architecture questions, deciding where new code belongs, reviewing coding standards, or understanding the layered command/parser/facade design.
pull-request-review
by ruby-gitReviews pull requests against project standards and posts review comments via the gh CLI. Use when reviewing PRs, checking coding standards compliance, or performing approval reviews.
refactor-command-to-commandlineresult
by ruby-gitMigrates a command class that still performs parsing or custom execution logic to return raw Git::CommandLineResult, moving parsing to facade/parser layers. Use during architectural redesign refactoring.
release-management
by ruby-gitPrepares and publishes new releases of the ruby-git gem including version bumps, changelog updates, tagging, and gem publishing. Use when preparing a release or checking release readiness.
review-arguments-dsl
by ruby-gitAudits a command class's arguments DSL definition to verify it accurately maps Ruby call arguments to git CLI arguments in the correct order with correct DSL methods and modifiers.
review-backward-compatibility
by ruby-gitAudits Git::Lib methods for backward compatibility after commands are moved to Git::Commands::* classes. Use to verify that migrated commands maintain their existing public API.
reviewing-skills
by ruby-gitAudits Agent Skills for quality, discoverability, and adherence to best practices. Use when reviewing a skill, checking skill quality, auditing skill descriptions, or validating skill structure before committing.
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.