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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compresso
by akillnessAI agent skill for CompressO — a free, open-source, offline desktop tool for batch video and image compression built with Tauri + React. Use when the user needs to compress, trim, convert, or embed subtitles into video/image files locally without any network dependency. Covers installation (Homebrew, DMG, MSI, AppImage, DEB), build from source (Rust + Node.js + pnpm), and guidance on FFmpeg/pngquant/jpegoptim/gifski pipelines. Triggers on: compresso, compress video, compress image, batch compression, ffmpeg compression, tauri desktop compression, offline video compress.
payloadcms
by akillnessOperate Payload CMS (Next.js-native headless CMS) in repo workflows: bootstrap a Payload app, configure collections/globals, run local dev + migrations, and ship safe content-model changes. Use when the request mentions Payload CMS, payload config, collection schema, admin panel, or Next.js + headless CMS integration.
claudekit
by akillnessUse this skill when you need a standardized Claude Code workflow toolkit. It covers claudekit plugin installation, init-wizard scaffolding for rules/modes/hooks/MCP, and safe operating guidance for team adoption.
lmstudio-cli
by akillnessOperate LM Studio's `lms` CLI and local/remote LM Studio servers for model discovery, server status checks, model loading, endpoint smoke tests, and downstream OpenAI-compatible wiring. Use when the user mentions LM Studio, `lms`, a local model server, `/v1/models`, a remote LM Studio host, or wants to connect another tool to LM Studio; even if they only ask to test a local OpenAI-compatible endpoint or choose the correct loaded-model identifier. Triggers on: lmstudio, lm studio, lms, local model server, LM Studio API, LM Studio endpoint, /v1/models, connect Strix to LM Studio, load model in LM Studio.
ooo
by akillnessRun the Ouroboros specification-first development loop: reduce ambiguity with a Socratic interview, freeze an immutable seed/spec, execute against that contract, verify before claiming success, and keep looping until completion is actually verified. Use when the user wants spec-first clarification, immutable requirements, drift-aware implementation, or a persistent completion loop that should keep going until tests / checks / acceptance criteria pass. Triggers on: ooo, ouroboros, interview, seed, run workflow, evaluate, evolve, ooo ralph, specification first, socratic interview, ambiguity reduction, persistent completion.
scaffold-exercises
by akillnessUse this skill when > Create exercise directory structures for educational content that comply with linting standards. Sections use XX-section-name/ naming, exercises use XX.YY-exercise-name/ with problem/, solution/, explainer/ variants. Use when creating course content or educational exercise structures.
ohmg
by akillnessAdopt `oh-my-agent` from a Gemini CLI or Antigravity workflow. Use when the user wants a portable multi-agent harness with `.agents/` as the source of truth, Gemini-native generated agents, Antigravity compatibility, or cross-vendor-ready orchestration that can later route work to Claude or Codex. Also use when mapping Claude workflows such as `team`, `autopilot`, `ultrawork`, or `ultraqa` into OMA workflows for Gemini or Antigravity. Triggers on: ohmg, oh-my-agent, oma, Gemini multi-agent, Antigravity agent team, .agents, portable harness, Gemini native agents, autopilot, ultrawork, ultraqa, and Gemini/Antigravity orchestration. Route Claude-first runtime orchestration to `omc` and Codex-first runtime orchestration to `omx`.
ultrawork
by akillnessRun a high-parallelism work burst for independent implementation or cleanup lanes. Use when the user explicitly invokes `$ultrawork`, `$ulw`, `ultrawork`, or asks to parallelize separable work quickly. Choose `team` when workers need shared task state, and choose `ultraqa` when the work is primarily QA/review.
spec-stack
by akillnessCompose GitHub spec-kit, Ouroboros (ooo), and HKUDS CLI-Anything into one spec-driven delivery stack — Write → Freeze → Run, verified. Author the spec with `/speckit.*`, freeze it as an immutable ooo seed with machine-checkable acceptance criteria, arm the loop with agent-native CLI harnesses from CLI-Hub (`--json` output as evaluate evidence), and loop until verification passes. Routes three patterns — full-stack (spec-kit → ooo → cli-anything), loop-only (ooo), docs-only (spec-kit) — with explicit anti-patterns (two spec SSOTs, harness-first builds, seedless ralph loops). Use when the user wants spec-to-verified-artifact delivery, wants spec-kit and ooo to work together without fighting, or needs real software driven inside a verified loop. Triggers on: spec-stack, spec stack, write freeze run, spec to verified, speckit + ooo, ooo + cli-anything, spec-driven loop, verified delivery stack, seed with tools.
npm-git-install
by akillnessRoute Node package-delivery ambiguity into one install packet: temporary Git bridge, SHA-pinned shared bridge, private-auth Git path, tarball / `npm pack` artifact, workspace / `file:` inner-loop, or publish-first registry handoff. Use when the user wants to install an npm / pnpm / Yarn / Bun package from a branch, tag, commit, fork, private repo, monorepo package, or unreleased fix, and the real question is which delivery path is safest rather than how Git or package registries work in general. Triggers on: npm install from GitHub, git dependency, github:owner/repo, git+ssh, git+https, private package from repo, install branch vs commit, monorepo package install, npm pack vs git, and should we publish this instead.
pydantic-ai
by akillnessBuild typed LLM applications with PydanticAI: schema-constrained outputs, tool integration, validation, retries, and deterministic downstream handoffs. Use when users need reliable structured outputs instead of free-form text generation.
obsidian
by akillnessUnified Obsidian skill: plugin development AND desktop automation. Use when building or validating an Obsidian plugin (boilerplate, 27 ESLint rules, submission), or when driving desktop Obsidian from the terminal via the official CLI, obsidian:// URIs, or kepano/obsidian-skills markdown/bases/json-canvas patterns. Triggers on: obsidian plugin, obsidian cli, obsidian automation, obsidian development, obsidian eslint, obsidian submission, obsidian markdown, obsidian bases, json-canvas, obsidian vault, obsidian URI, obsidian commands. Installable as a plugin: claude plugin marketplace add akillness/oh-my-skills
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.