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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api-reference
by videojsScaffold API reference documentation for Video.js 10 components and utility APIs. Validates the api-docs-builder output, checks design docs and linked PRs for context, creates the MDX reference page with anatomy, prose sections, demos, and the ComponentReference/UtilReference component. Triggers: "api reference", "reference page", "scaffold api docs", "add api docs", "component reference", "util reference", "hook reference".
aria
by videojsReview and implement accessibility patterns for UI components following WAI-ARIA and WCAG 2.1. Use when auditing code for a11y issues, implementing accessible controls, adding ARIA attributes, fixing keyboard navigation, handling focus management, building screen reader support, or implementing media player accessibility. Triggers on "accessibility review", "a11y", "ARIA", "keyboard navigation", "screen reader", "focus management", "WCAG", "captions", "live region".
component
by videojsBuild accessible, headless UI components with modern architecture patterns. Use when creating component libraries, design systems, or reusable UI primitives. Handles compound components, state management, accessibility, styling hooks, and API design. Includes HTML (controllers, ReactiveElement) and React (hooks, context) patterns. Triggers: "create component", "component pattern", "compound component", "headless component".
css-to-tailwind
by videojsMigrate vanilla CSS, CSS modules, SCSS, or styled-component styles to Tailwind v4 utilities. Prefer @theme-backed tokens and semantic utilities; document arbitrary values and parity gaps. For audits, load review/workflow.md. Triggers: "CSS to Tailwind", "migrate to Tailwind", "tailwind migration", "styled-components to Tailwind", "SCSS to Tailwind", "review Tailwind classes", "tailwind parity".
docs
by videojsWrite and review documentation for Video.js 10 — site pages, READMEs, and JSDoc. Use for writing concept pages, how-to guides, package READMEs, and inline API docs. Component reference pages use the `api-reference` skill instead. Triggers: "write docs", "create guide", "review docs", "audit documentation", "write README", "JSDoc", "inline docs".
split-behavior
by videojsSplit one SPF behavior into N with axis-declared, constraints-audited discipline. Forces explicit axis declaration (per-type horizontal vs. per-concern vertical) and a cross-boundary constraint audit before the split lands — avoids the failure mode where an apparent per-type-friendly behavior ships a split that quietly drops a cross-type ordering invariant the merged code was enforcing. Use after /refactor-behavior's decomposition check has concluded "split," or when you've already noticed a behavior wants splitting. Triggers: "split this behavior", "split into per-type", "extract per-type behaviors", "convert to per-type variants", "split behavior".
spf-update-behavior
by videojsUpdate an existing SPF behavior whose purpose is changing or expanding. Distinct from /refactor-behavior, which preserves purpose — this skill handles cases where the behavior gains new responsibility (new state slot to react to, new lifecycle phase, new constraint, new code path). Carries /refactor-behavior's purpose-first discipline applied to the *purpose change*. Triggers: "update behavior", "extend behavior", "modify behavior", "change behavior purpose", "expand behavior responsibility".
spf-implement-use-case
by videojsImplement a use-case composition documented in the SPF use-case-composition registry. Consumes a use-case doc at internal/design/spf/use-cases/<name>.md and produces the engine-side code: a variant engine factory, a parallel adapter, composition wiring, use-case-specific behaviors (if any), and tests. The implementation analog of /spf-document-use-case (which produces the doc; this consumes it). Walks through disambiguation + routing (verify the request is actually a use case, check constituent-feature implementation status), resolves the doc's open questions with the user, maps phases to chunks, routes to downstream skills (/spf-implement-feature for unimplemented constituents, /spf-create-behavior, /spf-update-behavior, /refactor-behavior), and updates both the use-case doc and constituent feature docs as code lands. Treats the use-case doc as a starting point for planning, not a hardened specification. Triggers: "implement use case", "implement SPF use case", "implement use-case composition", "build use ca
spf-implement-feature
by videojsImplement a feature documented in the SPF feature registry. Consumes a feature doc at internal/design/spf/features/<name>.md and produces the engine-side code: new behaviors, updates to existing behaviors, media-layer / network-layer primitives, and tests. The implementation analog of /spf-document-feature (which produces the doc; this consumes it). Walks through resolving the doc's open questions before coding, maps phases to discrete chunks, applies the SPF conventions catalog, routes to downstream skills (/spf-create-behavior, /spf-update-behavior, /refactor-behavior) per chunk shape, and updates the feature doc's Status / Implementation surface / Verification sections as code lands. Triggers: "implement feature", "implement SPF feature", "build feature", "code feature", "scope feature implementation", "implement <feature-name>".
spf-document-use-case
by videojsProduce or update an entry in the SPF use-case-composition registry at internal/design/spf/use-cases/. Triangulates context from multiple sources (Notion, GitHub, pasted writeups, existing use-case docs, constituent feature docs, codebase), grounds the use case in the four composition mechanisms (subtract / add / alternative-impl / alternative-default-config), applies use-case-specific cross-cutting concern checks, drafts the doc at the appropriate definition depth, and cascades narrow updates to constituent feature docs and sibling use cases. Triggers: "document use case", "register use case", "use case doc", "update use case doc", "deepen use-case stub", "new SPF use case", "use case composition", "draft use-case registry entry", "new use case composition".
spf-document-feature
by videojsProduce or update an entry in the SPF feature registry at internal/design/spf/features/. Triangulates context from multiple sources (Notion, GitHub, pasted writeups, existing feature docs, codebase), grounds the feature in the cluster heuristics, applies cross-cutting concern checks, drafts the doc at the appropriate definition depth, and cascades narrow updates to related feature docs. Triggers: "document feature", "register feature", "feature doc", "update feature doc", "deepen feature stub", "draft feature registry entry", "new SPF feature".
spf-create-behavior
by videojsCreate a new SPF behavior with conventions-aligned shape. Walks through purpose articulation (carries /refactor-behavior's purpose-first discipline), signal type choice, slot map design, composition placement, cleanup pattern selection, test placement, and engine wiring. Distinct from /refactor-behavior (which modifies an existing behavior preserving its purpose) and /spf-update-behavior (which modifies an existing behavior whose purpose is changing). Triggers: "create behavior", "new behavior", "create SPF behavior", "add behavior", "scaffold behavior", "new SPF behavior".
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.