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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cabloy-workflow
by cabloyUse this skill first when a Cabloy request is about choosing the right path before implementation: whether the work belongs to Vona backend scaffolding, Zova frontend scaffolding, backend/frontend contract sync, or docs, .docs-internal, CLAUDE.md, commands, or skills; which Cabloy Basic or Cabloy Start assumptions apply; or where Cabloy guidance should live. Trigger on requests that ask to route, classify, choose a workflow, choose an edition-specific path, or decide between docs, rules, and skills. Do not use it once the task is already clearly a backend scaffold, frontend scaffold, or contract-loop job.
cabloy-contract-loop
by cabloyUse this skill whenever a Cabloy task crosses the Vona-to-Zova contract boundary: backend DTO, controller, validation, entity, inferred DTO, or OpenAPI changes that should drive SDK, schema, api, model, or rest-output regeneration, or stale generated frontend consumers that may be out of sync with backend truth. Trigger for requests about stale home-api output, OpenAPI regeneration, whether to regenerate instead of hand-patching types, or how to verify the Cabloy Basic or Cabloy Start contract loop end to end. Prefer it when the main problem is backend/frontend sync or diagnosis, not initial backend scaffolding or frontend page/component scaffolding.
cabloy-backend-scaffold
by cabloyUse this skill whenever the user wants the Vona backend path in this Cabloy repo: scaffold or extend modules, beans, controllers, services, models, entities, DTOs, CRUD resources, migrations, indexes, validation, OpenAPI-facing backend files, or backend tests. Trigger for questions about which npm run vona generator or CRUD command to use and what backend follow-up is required after generation, especially when the user mentions create:bean, CRUD, meta.version, field indexes, DTOs, or tests. Prefer it for backend-first requests, even if they may later require frontend contract regeneration. Do not use it for frontend-first Zova work or stale generated consumer diagnosis.
cabloy-frontend-scaffold
by cabloyUse this skill whenever the user wants the Zova frontend path in this Cabloy repo: create or extend pages, components, api or model beans, route/query/params work, metadata refresh, SSR-sensitive frontend work, or component props, v-model, and generic refactors. Trigger for questions about which npm run zova create or refactor command to use and what frontend follow-up is required after generation, especially when the user wants the Zova way instead of generic Vue advice. Prefer it for frontend-first requests, even if backend context exists in the story. Do not use it for pure Vona scaffolding or backend/frontend contract-sync diagnosis.
cabloy-resource-field-update
by cabloyUse this skill whenever the user wants to update a field on an existing Cabloy backend resource: add a new persisted field, refine validation, add enum-like constraints, attach or change ZovaRender.field / ZovaRender.cell metadata, decide whether vonaModule.fileVersion should change, or demonstrate a custom frontend renderer for a backend field. Trigger when the request is specifically about modifying an existing resource field thread rather than creating a new CRUD/resource thread. Prefer it for backend-first field-update work that may branch into renderer-aware frontend follow-up. Do not use it for initial backend scaffolding, generic frontend scaffolding, or stale generated contract diagnosis.
cabloy-zova-source-reading
by cabloyUse this skill when the user wants to read, trace, or explain Zova frontend source code rather than scaffold new code: where a page/component/model/behavior/SSR flow is implemented, how a runtime path works internally, why a plain controller field is reactive, how Zova differs from generic Vue 3 habits, or which files to read first. Trigger for requests about Zova source reading, runtime tracing, Vue-vs-Zova explanation, controller/bean/IoC mental models, or the Zova-native way to understand frontend behavior. Do not use it for normal frontend scaffolding or backend/frontend contract regeneration.
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.