381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 17 skills
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voicebox

by marvelousempire
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Voicebox — Open-source AI voice studio for cloning, dictation, and voice creation workflows. Use when the user mentions or needs: Voicebox; open source voice clone studio; local voice AI workstation.

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schedule Updated 1 month ago
marvelousempire

ltx-2

by marvelousempire
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LTX-2 — Official Python inference and LoRA trainer package for the LTX-2 audio-video generative model (Lightricks). Use when the user mentions or needs: LTX-2; Lightricks LTX; LTX-2 inference; LTX-2 LoRA training; audio-video generative model Python.

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schedule Updated 1 month ago
marvelousempire

nephew-cloak-amending-mission

by marvelousempire
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Nephew CLOAK executive layer — full Amending Mission. When Grok or another AI designed systems (whitepapers, agents, repos, merges) but only simulated git, systematically verify every claim against GitHub, rebuild missing artifacts, witness commits, and maintain a green-check ledger. Internalizes Automata as Layer-0 belief system. Triggers: Amending Mission, simulated pushes, fix Grok lies, CLOAK activation, turn chat into real repos, Nephew enforcer of truth.

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schedule Updated 1 month ago
marvelousempire

sdk

by marvelousempire
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Guide users building apps, scripts, CI pipelines, or automations on top of the Cursor TypeScript SDK (`@cursor/sdk`). Use when the user mentions integrating, installing, or writing code against the Cursor SDK; says `Agent.create`, `Agent.prompt`, `Agent.resume`, `agent.send`, `run.stream`, `CursorAgentError`, or `@cursor/sdk`; asks to run Cursor agents programmatically from a script, CI/CD pipeline, GitHub Action, backend service, or other code outside the Cursor IDE; wants to pick between local and cloud runtime, configure MCP servers for an SDK agent, or handle streaming, cancellation, or errors; or is wiring Cursor into an automation, bot, or REST `/v1/agents` migration. Use eagerly rather than answering from memory; the SDK surface evolves and this skill is the source of truth for the external package.

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marvelousempire

emil-design-eng

by marvelousempire
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Encodes Emil Kowalski's design engineering philosophy for UI polish, component design, animation decisions, and invisible details that make software feel great.

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marvelousempire

colima-docker-swap

by marvelousempire
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Detect a wedged Docker Desktop on macOS Tahoe + Apple Silicon and swap in Colima as the docker runtime — native arm64, same docker socket, no Electron VM. Installs colima + lima as direct binaries to `~/.local/bin/` (no brew, no sudo) when Homebrew is broken. Pairs with Dockyard for the UI. Use when `docker info` hangs, Docker Desktop won't start, or the user says "Docker is broken / I don't see anything in Docker."

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marvelousempire

ai-proposal-review-inbox

by marvelousempire
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Pattern for letting an AI agent "grow" a hand-curated source file (cleaners, rules, fixtures, prompts, library entries) without ever auto-mutating it. AI proposes → record lands in a review inbox → user accepts → server generates a paste-ready snippet → user pastes into source and commits. Canonical source stays human-authored. Triggers on "AI proposes new", "let the AI add to", "review inbox", "paste-ready snippet", "never auto-edit source", "AI suggestion queue", "human-in-the-loop AI tool", "self-extending library", "propose new cleaner", "propose new rule", "propose new fixture".

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marvelousempire

doctrine-decanter-sorting-agent

by marvelousempire
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Sorts messy PRDs, doctrine dumps, and chat-export planning files into a professional docs tree: re-number stable IDs, de-duplicate (canonical on top, duplicates at bottom with clear spacing), split monoliths into bible/shaders/ product folders, preserve archives, and wire README indexes. Use when the user asks to structure a project folder, break into folders, split a PRD, decanter, de-duplicate, re-number requirements, move doubles to the bottom, organize cinematic fluid experience-style docs, or clean up combined single-file dumps.

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marvelousempire

skill-nutrients-decanter

by marvelousempire
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A "Skill Nutrient" is a learning moment that is critically vital and net positive — a plus, not a minus or zero. The Decanter is the process that scrapes, filters, and compounds only those nutrients from any conversation, session, or project arc into permanent repo improvements. Not everything extracted from a session is a nutrient — only what genuinely adds strength, clarity, reusability, or safety. This skill defines both the concept (what counts as a nutrient) and the decanting process (how to extract, filter, and file them). Triggers on "skill nutrients", "decant the lessons", "what are the nutrients from this session", "only keep what's a plus", "compound the learning", "filter what's worth filing", "nutrient extraction", "what genuinely adds value here", "Skill Nutrients Decanter".

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schema-fk-typecheck

by marvelousempire
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Before writing a CREATE TABLE column with a REFERENCES clause, look up the parent table's column type so the FK declaration matches. Catches BIGINT vs UUID, SERIAL vs BIGSERIAL, INTEGER vs TEXT mismatches at write time — not at production deploy.

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canvas

by marvelousempire
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A Cursor Canvas is a live React app that the user can open beside the chat. You MUST use a canvas when the agent produces a standalone analytical artifact — quantitative analyses, billing investigations, security audits, architecture reviews, data-heavy content, timelines, charts, tables, interactive explorations, repeatable tools, or any response that benefits from visual layout. Especially prefer a canvas when presenting results from MCP tools (Datadog, Databricks, Linear, Sentry, Slack, etc.) where the data is the deliverable — render it in a rich canvas rather than dumping it into a markdown table or code block. If you catch yourself about to write a markdown table, stop and use a canvas instead. You MUST also read this skill whenever you create, edit, or debug any .canvas.tsx file.

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migrate-to-skills

by marvelousempire
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Convert 'Applied intelligently' Cursor rules (.cursor/rules/*.mdc) and slash commands (.cursor/commands/*.md) to Agent Skills format (.cursor/skills/). Use when you want to migrate rules or commands to skills, convert .mdc rules to SKILL.md format, or consolidate commands into the skills directory.

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Page 1 of 2

Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.