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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antislop-codebase
by swyxioAnalyze and transform messy, prototype, overgrown, slop-prone, or hard-to-maintain software repositories into maintainable product-shaped codebases while preserving existing product behavior. Use when the user asks to antislop a codebase, clean up a messy repo, run a maintainability migration, write a refactor plan, modernize structure, improve TypeScript/type boundaries, harden tests, reduce large files, clean architecture, coordinate subagent-driven refactors, or produce a final migration audit/report/microsite. Do not use for broader production-readiness specialties such as security audits, observability/logging programs, compliance hardening, SRE/runbook work, or reliability engineering unless the user explicitly scopes those as part of the maintainability refactor.
europe-developer-api
by swyxioUse when working with AI Engineer Europe 2026 developer-facing endpoints, public schedule JSON, speakers JSON, llms.txt files, MCP access, and the local aieng CLI.
podcast-publishing-assistant
by swyxioTranscribe long-form audio, YouTube videos, podcasts, interviews, or panels; summarize them; extract chapter markers; and draft publishing assets like titles, YouTube descriptions, show notes, and tweet/X copy. Use when a user shares a YouTube link or audio/video file and asks for transcription, diarization, summaries with timestamps, chapter markers, show notes, titles, descriptions, promo copy, or social posts. Especially use for Whisper-based workflows, source-audio-first podcast processing, and any request to turn a recorded conversation into publishable assets.
thumbnail-extraction
by swyxioExtracts the most interesting frames from video files for thumbnail compositing. Detects faces, expressions, smiles, and presentation slides. Outputs full frames, face crops, and transparent cutouts. Use when asked to extract thumbnails, find interesting frames, grab screenshots from video, or create thumbnail candidates from recordings.
slackbot-builder
by swyxioBuild production Slack bots as a maturity ladder (L0–L6): signature verification, fast acks + event idempotency, threads-as-sessions, responsive feedback (reactions, status, live streaming), Block Kit interactions + human-in-the-loop approvals, the native Agents & AI Apps surface, file/media outputs + settings modals, durable long-running work, rate-limit + security hardening, model-call tracing, and multi-surface/multi-tenant scale. Use when building or hardening any Slack app, bot, agent-in-Slack workflow, or Slack Events API / Block Kit / slash-command integration.
youtube-api
by swyxioManage YouTube videos programmatically via the YouTube Data API v3 — upload video files, upload custom thumbnails, update video metadata (titles, descriptions, tags), and query video/channel info without touching YouTube Studio's browser UI. Use this skill whenever the user wants to: upload a local video file to YouTube; set, update, or change a YouTube video's thumbnail from a local image file; batch-set thumbnails across multiple videos; update video titles, descriptions, or tags programmatically; query their channel's video list; or do anything involving the YouTube Data API. Triggers: "upload to youtube", "upload video", "set thumbnail", "upload thumbnail", "change thumbnail", "YouTube API", "batch thumbnails", "update video title", "update video description", "youtube metadata", or any reference to programmatically managing YouTube videos. Also use when the browser-based YouTube Studio upload is unreliable or slow.
public-qa-chatbot
by swyxioBest practices for building an unauthenticated public Q&A chatbot widget. Covers rate limiting, security hardening, cost optimization, semantic caching, observability, UX patterns, chat scroll behavior, and architecture. Tech-agnostic with concrete examples from a production implementation.
youtube-publish
by swyxioThis skill should be used when the user asks to "upload videos to YouTube", "publish videos on YouTube", "set YouTube titles and descriptions", "add timestamps to YouTube videos", or needs to edit video metadata, assign playlists, and publish in YouTube Studio.
zoom-download
by swyxioThis skill should be used when the user asks to "download Zoom recordings", "grab recordings from Zoom", "get new Zoom videos", or needs to download cloud recordings and analyze their content via frame extraction. Covers file type selection, filename verification, and ffmpeg-based content analysis.
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.