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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max-subagents-parallel
by ahostbrUse when starting ANY non-trivial task to maximize parallelism - spawns multiple subagents, creates task graphs, identifies independent work streams
k-plan
by ahostbrUse when creating a structured implementation plan with parallel task decomposition for team execution. Triggers on 'k-plan', 'create a plan', 'plan this task', 'decompose this work', or when the user needs a detailed plan document with task breakdown, dependencies, and team assignment before implementation.
find-skill-sh
by ahostbrUse when starting work on a specific technology (typescript, react, python, vite, nextjs, etc), when user asks "find a skill for X", "search skills.sh", "best practices for X", or when you need procedural knowledge for a task. Searches the open agent skills ecosystem at skills.sh.
ls-comfy-to-liteimage
by ahostbrTranslate ComfyUI workflows into LiteImage API calls. Use when the user provides a ComfyUI workflow JSON, mentions ComfyUI, asks about node graphs, wants to convert or replicate a ComfyUI pipeline, or references ComfyUI templates. Also use when the user asks how to do something 'like in ComfyUI' or wants to run a workflow locally. Triggers on 'comfy', 'ComfyUI', 'comfyui', 'workflow JSON', 'node graph', 'convert this workflow', 'translate this workflow', 'run this workflow', 'I have a workflow', 'KSampler', 'LoraLoader', 'exported workflow', 'comfy template', 'what this comfy workflow does', 'equivalent in LiteImage', 'how do I do that here', 'make it work here'.
ls-tts
by ahostbrToggle TTS mode — when ON, all Claude responses are spoken aloud via Edge TTS (en-GB-SoniaNeural / Sonia). Simple on/off toggle.
ls-eva
by ahostbrUse when the user launches Claude from inside the LiteEVA panel, wants to create or work on a video project, or asks about agentic video editing. Subcommands — remotion, voiceover, music, ffmpeg. Triggers on 'LiteEVA', 'open EVA', 'launch EVA', 'video project', 'start a video', 'new video', 'EVA panel', 'video editor', 'create video', 'work on video', 'remotion', 'composition', 'render video', 'voiceover', 'TTS', 'generate narration', 'generate music', 'soundtrack', 'ACE-Step', 'ffmpeg', 'trim video', 'compress video', 'platform export'.
ls-max-swarm
by ahostbrUse when spawning multiple coding agents to work on independent files or modules simultaneously. Triggers on 'max-swarm', 'swarm this', 'swarm it', 'spawn a swarm', 'coding swarm', 'one agent per file', 'throw agents at this'. NOT for structured decomposition (use /max-parallel).
ls-generative-ui
by ahostbrLiteSuite Generative UI — render interactive widgets inline in Frontier Chat / Sentinel Chat. Use prompt_widget when you need the user's input to continue (confirmations, forms, picks). Use render_widget for fire-and-forget displays (charts, stat cards, dashboards). Triggers on 'render widget', 'prompt widget', 'ls-generative-ui', 'show me a chart', 'ask the user', 'confirm before', 'show a form', or whenever a visual or interactive answer is clearer than prose.
ls-conversation-lookup
by ahostbrFind, search, and summarize Claude Code conversations. BM25 + semantic (vector) + hybrid search across indexed messages. Uses all-MiniLM-L6-v2 embeddings (sentence-transformers). USE PROACTIVELY when you need historical context — don't rg through JSONL files manually. Triggers on 'find conversation', 'search conversations', 'search convos', 'convo search', 'what conversation did we', 'which session did we', 'when did we build', 'find where we discussed', 'remember when we', or when a bare 8-char hex ID is given. Also use when the user references past work and you need to locate it. Use '--mode semantic' for conceptual queries, '--mode hybrid' for best results.
ls-max-parallel
by ahostbrUse when decomposing a task into parallel subtasks with structured subagent spawning. Triggers on 'max parallel', 'parallelize this', 'spawn subagents', 'decompose and parallelize', 'break this into parallel tasks', 'recursive decomposition'. NOT for simple agent swarms (use /max-swarm).
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.