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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video-compose
by Utopai-ResearchGenerates and prompts video clips on the filmmaking canvas. Use when the user asks to generate, render, animate, continue, restyle, edit, shoot, or compose a video clip; render script or shot notes as video; animate a storyboard, starting frame, image, character, location, or reference; use image, video, audio, storyboard, starting-frame, or voice refs; compose an ad, brand film, product promo, music-video shot, or video sequence; or before calling generate_video.js. Owns video CLI flags, refs, prompt construction, audio-ref handling, and video-specific failure hints.
story-to-video-workflow
by Utopai-ResearchOrchestrates story, script, screenplay, concept, product promo, and multi-shot idea work into finished video. Use first when the user asks to make a video from a story or script; asks what next in a story video project; or needs a decision spanning script splitting, image refs, voices or VO, video clips, render strategy, Timeline ordering, or final Timeline handoff. Routes execution to script-compose, image-compose, voice-compose, and video-compose before those skills' CLIs are used.
voice-compose
by Utopai-ResearchDesigns and attaches voice samples or final narration/line audio on the filmmaking canvas via the local generate_voice.js CLI. Use before calling generate_voice.js; when the user asks to give a character a voice, preview how a character sounds, create reusable timbre anchors for every speaking character or VO/narration, or create exact narration/VO/final line audio.
groups-compose
by Utopai-ResearchDesigns and maintains semantic groupings and readable layouts on the filmmaking canvas — scenes, character-reference sets, act beats, and other titled visual frames. Use when nodes on the canvas cluster around a shared meaning and would read more clearly if arranged together and wrapped in a frame. Don't force it — groups are a view concern, not an organizing tax.
image-compose
by Utopai-ResearchGenerates/edits filmmaking canvas images via generate_image.js and generate_image_pro.js. Use before image CLIs for character/location design, refs, starting frames, storyboards, stills, edits, variations, and downstream video anchors. Video-bound characters default to Pattern 7 4-panel sheets; one-off static portraits use Pattern 1. Story/script breakdowns should create detailed location anchors plus material character/location variants. Storyboards use Pattern 6: one pro composite mosaic per clip/<=15s shot note.
script-compose
by Utopai-ResearchHandles explicit screenplay/story work on the filmmaking canvas. Triages screenplay (use verbatim), story/concept (iterate then rewrite), or neither (defer). Captures the final script note/title; on explicit command, splits into <=15s shot notes and extracts characters, variants, locations, and speaking/VO needs. Use for writing, adapting, rewriting, splitting, analyzing, or breaking down scripts/stories. Preserves dialogue verbatim and returns multi-stage planning to story-to-video-workflow before media generation. Does not split/analyze on file drop or without explicit intent.
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.