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...
dial-e2e-testing
by epamReference for writing ai-dial-chat Playwright e2e tests - dialFixtures composition, page object and assertion patterns, test data builders, GeneratorUtil naming, setTestIds, dialTest.step structure, and anti-patterns. Use when creating or fixing e2e tests, adding fixtures, page objects, assertions, or test data builders in apps/chat-e2e.
dial-api-patterns
by epamReference for writing ai-dial-chat Next.js API routes - session validation with next-auth, the validate-session/validate-input/proxy pattern, streaming responses from DIAL Core, error handling, and status-code conventions. Use when creating or editing any handler under apps/chat/src/pages/api/.
dial-testing
by epamReference for testing ai-dial-chat - Vitest + @testing-library/react unit tests (Arrange/Act/Assert, vi.mock + vi.hoisted), Playwright e2e with dial fixtures and page objects, test locations, and run commands. Use when writing or fixing unit or e2e tests, setting up mocks, or running the test suites.
chat-release-notes
by epamUse when the user asks to enhance, refine, polish, or "look at" the release notes for a tag — typically a fresh CI-generated pre-release (e.g. `0.45.0-rc.55`) or a stable cut. Reads the auto-generated notes off the GitHub release, classifies and rewrites each bullet in this project's editorial voice, builds the `Deployment Changes` section from `apps/chat/README.md` / PR bodies / source, and saves a draft to `claude/release-notes/`. Never edits GitHub directly.
dial-development
by epamReference for ai-dial-chat code style and conventions - naming (PascalCase components, kebab-case files), import order, React FC component pattern, Redux hooks/epic patterns, lint/format/typecheck commands, conventional commits, and the pre-PR checklist. Use when writing components, hooks, or store code, or preparing a commit/PR.
dial-architecture
by epamReference for the ai-dial-chat architecture - NX monorepo layout, Redux Toolkit + RxJS epics, Next.js API proxy, store/selectors/actions barrels, layer responsibilities and dependency rules. Use when adding a new store domain or API route, wiring up epics/selectors/actions, or reasoning about how data flows from a component through the store to DIAL Core.
uui-components
by epamHelps create and modify UUI (EPAM Unified UI) components following established patterns. Use when creating new components, modifying existing components, working with withMods, component props, styling, or component architecture in the UUI library.
uui-data-sources
by epamHelps work with UUI DataSources (ArrayDataSource, LazyDataSource, AsyncDataSource) powering PickerInput, DataTable, FiltersPanel, and other data-driven components. Use when implementing or fixing features that load, filter, sort, or display lists of data.
uui-documentation
by epamHelps update UUI documentation, add doc examples, configure Property Explorer, and manage component API documentation. Use when adding documentation examples, updating Property Explorer configs, generating API references, working with UUI documentation site, or when adding/removing/modifying public props on component interfaces.
uui-e2e-testing
by epamHelps create and maintain E2E and screenshot tests for UUI components using Playwright. Use when adding E2E tests, creating screenshot tests, updating preview configurations, or working with Property Explorer previews for testing.
uui-github-issue-workflow
by epamFetches GitHub issues from URLs and creates implementation plans for UUI. Use when the user provides a GitHub issue link (github.com/.../issues/N), asks to plan work from an issue, or implement/fix an issue by URL.
uui-pr-contributing
by epamGuides the UUI pull request process including branch naming, pre-PR checklist, changelog updates, and quality requirements. Use when preparing a pull request, writing commit messages, or following UUI PR requirements.
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.