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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update-release
by jasonraimondiAutomates the release process for the ts-oauth2-server project.
plan-bender-cli
by jasonraimondiReference for the plan-bender CLI (`pb` and `pba` / `plan-bender-agent`). Use when the user asks about plan-bender commands, the standalone merge command, worktree management, completion marker, the dispatcher skill, recovering a stuck issue, or Linear sync. Also triggers on "what does pb / pba do", "how do I run plan-bender", "pb merge", "pb status", "pb retry", or any question about plan-bender's command surface.
best-practices-comments
by jasonraimondiAudit code comments — flag and propose removal of journal, noise, redundant, mumbling, or nonlocal comments, and surface refactoring opportunities that would make a comment unnecessary. Use when the user asks to "review comments", "audit comments", "clean up comments", or check comment quality.
best-practices-css
by jasonraimondiProduce idiomatic, browser-aware modern CSS with progressive-enhancement fallbacks for vanilla CSS and Svelte styling tasks. Use for layout, spacing, typography, color/theming, responsive/container-query work, selector/specificity cleanup, and interactive UI styling, plus feature-compatibility lookups (tier, `bcd_id`, `@supports` fallbacks) for individual motion features. For motion *design* — easing systems, transform strategy, View Transitions choreography, Disney principles — defer to `css-motion-systems`. Do not use for non-styling business logic, or design critique without code edits.
best-practices-html
by jasonraimondiUse when writing or refactoring HTML or template markup (including Svelte), choosing between div/section/article/ul/li, cleaning up div soup, fixing heading or landmark structure, labeling forms, deciding between buttons and links, or replacing custom controls with native HTML.
best-practices-oauth2-server
by jasonraimondiOAuth2.0
best-practices-oidc
by jasonraimondiBest practices for OpenID Connect (OIDC) and OAuth 2.0 — flow selection, token validation, endpoints, discovery, and security hardening. Use when building, integrating, or reviewing authentication code: OIDC/OAuth clients (RP), authorization servers (OP), an OIDC library, ID token / JWT validation, PKCE, refresh tokens, DPoP, or logout. Triggers on OpenID Connect, OAuth, SSO, ID token, access token, auth flow, JWKS, client_secret, PKCE, or "log in with".
best-practices-svelte5
by jasonraimondiUse when creating, editing, reviewing, or refactoring .svelte components, .svelte.ts/.svelte.js modules, or SvelteKit applications
copy-to-clipboard
by jasonraimondiPipe the response's prose to the macOS clipboard via `pbcopy` so the user can paste it directly. Use when the user says "copy to clipboard", "/copy", "pbcopy this", "put it on my clipboard", or otherwise asks for paste-ready output. macOS only.
frontend-oklch
by jasonraimondiOKLCH color space for web projects. Convert hex/rgb/hsl to oklch, generate palettes, check contrast, and handle gamut boundaries. Triggers on oklch, color conversion, palette generation, contrast ratio, gamut, display p3, design tokens, hue drift, chroma, dark mode colors.
pr-description-template
by jasonraimondiRead an open GitHub pull request via the `gh` CLI and rewrite its title and description to follow a conventional-commits + JIRA-ticket format with a concise structured body and a smart pre-merge checklist derived from the diff. Use this skill whenever the user wants to clean up, format, normalize, standardize, or apply a template to a pull request — including phrases like "fix my PR description" or "update PR".
review-code
by jasonraimondiCode-review skill with mode routing. Default lens is `standard` (alias `quick`) — correctness, security, quality in one pass with a verdict. Other modes: `security` (OWASP, high-confidence only), `architecture` (module depth, coupling, boundaries), `merge-risk` (shippability + reversibility), `intent` (alias `adversarial`) — does the diff deliver what was promised, `multi` (alias `full`) — six parallel reviewers plus a verifier. Invoke as /review-code [<mode>] [<scope>]. Use for any code-review or PR-audit task.
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.