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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yoast-seo-wordpress-optimization-toolkit
by agentskillexchangeYoast SEO is one of the most widely deployed WordPress SEO plugins, and this skill turns its real analysis surface into a practical workflow for optimizing content, schema, metadata, and indexing behavior. It is aimed at WordPress operators who want agent help with SEO settings that map to the actual Yoast plugin and docs.
yoast-seo-wordpress-search-optimization-plugin
by agentskillexchangeYoast SEO is the long-running WordPress SEO plugin from Yoast, used to manage metadata, XML sitemaps, schema output, readability checks, and search appearance settings from inside wp-admin. It fits content teams and site operators who need repeatable on-page SEO controls without custom code for each site.
youtube-chapters-generator-with-whisper
by agentskillexchangeDownloads YouTube audio via yt-dlp, transcribes with Whisper, and uses NLP topic segmentation via TextTiling algorithm to auto-generate chapter markers with timestamps and titles.
yq-yaml-and-structured-data-processor
by agentskillexchangeProcess, query, and transform YAML, JSON, XML, CSV, TOML, and properties files from the command line using yq. Supports jq-like expressions for reading, updating, and converting between formats.
yazi-async-terminal-file-manager
by agentskillexchangeYazi is a blazing-fast terminal file manager written in Rust with async I/O, image previews, Vim keybindings, and a Lua plugin system. It integrates with ripgrep, fd, fzf, and zoxide for a seamless developer workflow in the terminal.
yeoman-scaffold-runner
by agentskillexchangeExecutes Yeoman generators via the yo CLI and yeoman-environment API to scaffold applications, components, and microservices. Manages generator discovery through the npm registry and supports sub-generator composition.
yeoman-generator-builder
by agentskillexchangeCreates custom Yeoman generators using the yeoman-generator API and yo CLI. Scaffolds generator packages with prompting, writing, and install phases, supporting composability via this.composeWith() for multi-generator workflows.
yeoman-enterprise-generator-suite
by agentskillexchangeManages Yeoman generators for enterprise application scaffolding with custom sub-generators. Handles Angular module generation via generator-angular, Express API scaffolding, and composite generators with shared prompting and conflict resolution.
yeoman-sub-generator-composition-builder
by agentskillexchangeOrchestrates Yeoman generator composition by chaining sub-generators via the Yeoman Environment API. Manages yo run loops, priority queues, and cross-generator dependency resolution.
yeoman-workflow-orchestrator
by agentskillexchangeOrchestrates Yeoman generator workflows with composable sub-generators and mem-fs-editor file transformations. Manages generator dependencies via yo env and supports custom inquirer.js prompt chains.
youtube-chapter-generator-from-transcripts
by agentskillexchangeExtracts YouTube video transcripts via the youtube-transcript-api Python library and generates semantic chapter markers. Uses sentence-transformers for topic segmentation and formats chapter timestamps for YouTube description metadata compliance.
yt-dlp-feature-rich-audio-and-video-downloader-cli
by agentskillexchangeyt-dlp is a powerful command-line tool for downloading audio and video from thousands of websites including YouTube, Vimeo, and social media platforms. It supports format selection, subtitle extraction, metadata embedding, SponsorBlock integration, and batch processing with extensive post-processing options.
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.