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...
tsdown
by juninmd**BUNDLER SKILL** - Fast library bundling using Rolldown and Oxc. USE FOR: building libraries, generating .d.ts files, multi-format bundling, standalone executables, tsup migration. DO NOT USE FOR: application bundling (use vite), non-JS/TS, legacy Rollup. INVOKES: tsdown cli, rolldown, oxc.
developing-nestjs
by juninmd**DEVELOPMENT SKILL** - Build enterprise-grade Node.js backends with NestJS. USE FOR: NestJS modules, controllers, services, class-validator, DTOs, Prisma integration, JWT authentication, exception filters. DO NOT USE FOR: frontend development, simple Express-like scripts without structure, non-Node.js backends. INVOKES: nest cli, prisma, jest.
developing-node
by juninmd**DEVELOPMENT SKILL** - Build and manage modern Node.js applications. USE FOR: dependency management (pnpm/npm), JS/TS build automation, lockfile reconciliation, pnpm migrations, Biome linting, Vite/SWC configurations. DO NOT USE FOR: client-side-only tasks (without build context), non-JS scripting (use developing-python/developing-go). INVOKES: pnpm, npm, npx, node, biome.
reviewing-skills
by juninmd**MAINTENANCE SKILL** - Audit and improve agent skills for compliance and clarity. USE FOR: skill frontmatter audit, YAML validation, improving skill descriptions, standardizing instructions, auditing .agents/skills/ directory. DO NOT USE FOR: implementing actual skill logic, general code linting (use auditing-code), project architecture (use improving-codebase-architecture). INVOKES: waza, yaml-lint, frontmatter validation.
modern-python
by juninmd**DEVELOPMENT SKILL** - Build modern Python apps with uv and Ruff. USE FOR: Python toolchain setup, uv migration, pyproject.toml, dependency management, Ruff, and ty configuration. DO NOT USE FOR: application feature implementation (use developing-python), Python older than 3.11, manual venv. INVOKES: developing-python, uv, ruff, ty.
pnpm
by juninmd**PACKAGE MANAGER SKILL** - Efficiently manage Node.js dependencies and monorepos using pnpm. USE FOR: pnpm, workspaces, frozen lockfiles, dependency overrides/patches, catalogs, disk space. DO NOT USE FOR: non-Node.js packages, pure backend logic, cloud infrastructure. INVOKES: pnpm cli, pnpm-workspace.yaml, .npmrc.
vite
by juninmd**BUNDLER SKILL** - Frontend bundling with Vite 8 and Tailwind v4. USE FOR: Vite config, Tailwind v4, dev server proxy, asset management, React/Vue plugins, Rolldown optimization. DO NOT USE FOR: npm library bundling (use tsdown), non-web JS, legacy Webpack migration. INVOKES: vite cli, tailwind v4, oxc.
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.