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.
Querying local SQLite index...
extension-features
by podman-desktopGuide for working with the Podman Desktop extension feature mechanism: declaring features in an extension manifest, how the registry propagates them to the renderer, and how Svelte components detect active features. Use when adding a new feature declaration to an extension, writing renderer code that reacts to an extension being active/inactive, or debugging why a feature flag is not being picked up.
mcp-testing
by podman-desktopInteractive testing of Podman Desktop UI using the electron-test MCP server. Supports both production (installed app) and development (pnpm watch) modes. Use for manually testing UI workflows, debugging UI issues, exploring features, or quick acceptance testing.
issue-requirements
by podman-desktopReads a GitHub issue and all its linked issues, PRs, and comments to extract requirements and context. Invoke for any request combining a GitHub issue reference (#123, owner/repo#123, or URL) with a need to understand what the issue asks for — extracting acceptance criteria, understanding scope, gathering requirements before implementation or testing, or analyzing what changed. Extracts only what is explicitly stated in the issue and its linked references — does not invent or assume. Not for writing code, running tests, or debugging.
storybook-record-video
by podman-desktopRecord MP4 videos of a Storybook component story in all four themes (light, dark, hc-light, hc-dark). Use when the user asks to record, capture, or grab a video of a Storybook story or component animation.
investigate-gh-run
by podman-desktopDeep investigation of CI/CD test failures - identifies root causes by analyzing logs, artifacts, git history, and source code
new-extension
by podman-desktopScaffold a new Podman Desktop extension as a standalone repository with all required boilerplate, build config, Containerfile, and Svelte webview. Use when the user asks to create a new extension, add extension boilerplate, scaffold an extension, or bootstrap a Podman Desktop extension project.
storybook-screenshot-docs
by podman-desktopCapture a full-page HiDPI (retina, 2x) screenshot of a Storybook component's Docs page in all four themes (light, dark, hc-light, hc-dark). Use when the user asks to screenshot, capture, or grab the Docs page, autodocs, or full documentation view of a Storybook component.
storybook-hmr
by podman-desktopApply or revert the local Storybook HMR patch that auto-rebuilds the UI package when source files change. Use when starting Storybook work, component modernization, or when the user mentions Storybook HMR or hot reload not working for UI components.
storybook-screenshot-story
by podman-desktopCapture HiDPI (retina, 2x) screenshots of a Storybook component story in all four themes (light, dark, hc-light, hc-dark). Use when the user asks to screenshot, capture, or grab an image of a Storybook story or component.
component-modernization
by podman-desktopPrepare and execute Design System component modernization. Drafts GitHub subtask issues, applies the Storybook HMR patch, and guides the modernization workflow. Use when starting work on a new component modernization, creating subtask issues, or when the user mentions design system modernization.
prototype
by podman-desktopSet up or tear down the UX prototype screen switcher in the Podman Desktop titlebar. Use when the user wants to start a new UI/UX prototype, add prototype screen states, or remove the prototype infrastructure after a prototype ships. Triggers: "set up a prototype", "new prototype", "prototype switcher", "remove prototype", "tear down prototype".
playwright-trace-analysis
by podman-desktopAnalyzes Playwright trace archives (`trace.zip`) to diagnose test failures, flaky behavior, and unexpected UI states using trace steps, console output, network activity, and screenshots. Use when the user provides a trace path or trace artifact, asks why a Playwright or E2E test failed, wants root-cause analysis from CI artifacts, mentions the trace viewer, or asks whether a failure is flaky or an application bug.
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.