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...
ktlo
by coinbaseInstructions to fetch assigned Linear issues in the current cycle and potentially kick off a development session.
cds-migrator-transform
by coinbaseUse when a CDS change in **cds-web**, **cds-common**, **cds-mobile**, **web-visualization**, or **mobile-visualization** needs a **jscodeshift** migration in `packages/migrator` to update callers or mitigate breaking API or import moves (add or change a transform, tests, or preset entry).
dev-cds-mobile
by coinbaseUSE THIS when asked to work on a new or existing (MOBILE) CDS React component in packages/mobile
figma-enumerate-component-urls
by coinbaseEnumerates every public component set in a CDS Figma file and produces a list of [Component Name]: [Figma URL] entries grouped by page section. Use this skill whenever asked to list, generate, or audit Figma component URLs, produce a component inventory from a Figma file, or when doing a Code Connect refresh/audit that requires knowing which Figma node each component maps to. Also trigger when someone asks "what are the Figma URLs for CDS components" or wants to find the node-id for a specific component.
cds-docs
by coinbaseRetrieve Coinbase Design System (CDS) documentation: setup, installation, theming, tokens, and per-component APIs/examples. Use this skill whenever the task involves CDS components, design-system rules, theming, or choosing between web and mobile CDS packages, even if the user only says "use CDS" or names a component. Always start from the docs route index, then fetch only the pages you need to reason and implement correctly. Prefer the CDS MCP server (`list-cds-routes`, `get-cds-doc`); if MCP is unavailable, use curl against https://cds.coinbase.com/llms/....
cds-design-to-code
by coinbaseTurns frontend designs from Figma into CDS-first React or React Native code. Use this skill whenever the user shares a Figma URL such as `figma.com/design/...?...node-id=...` while working in a frontend application context.
git-repo-manager
by coinbaseInstructions to manage a local cache of GitHub repositories. This would typically done in cases where the user want to perform research/analysis on a repository. Invoke whenever you need to clone a repo that isn't present locally, bring an existing clone up to date, or remove a repo from the cache. This skill handles only the mechanical filesystem/git operations — not research, analysis, or anything about the repo's contents.
git-detect-breaking-changes
by coinbaseAnalyzes the previous N commits for breaking changes across the CDS public API surface. Use this skill when you need to check if any recent changes will cause breaking changes in the CDS public API surface.
cds-code
by coinbaseProduces high quality Coinbase Design System (CDS) code for React and React Native projects. Always use this skill every time you are asked to create or update UI or write React or React Native code.
skill-creator
by coinbaseCreate new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
eslint-plugin-custom-rule
by coinbaseUSE THIS when asked to create a new eslint plugin rule for the eslint-plugin-cds package
cds-accessibility
by coinbaseReviews already-written Coinbase Design System (CDS) UI for accessibility: verifying documented accessibility props (e.g. accessibilityLabel, accessibilityState), confirming the chosen CDS primitives cover the right assistive technology behavior, and checking usage against official CDS documentation—not generic web ARIA tutorials. Use this skill to review CDS UI for screen reader, keyboard, and labeled control requirements after the code has been written.
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.