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...
tron-dashboard-creating
by merileyCreate production-ready Grafana dashboards for TRON team services including consumer dashboards, task metadata, RQ rules manager, and Kafka metrics. Use when building dashboards for task event consumers or RMS TRON services.
vendure-graphql-writing
by merileyExtend Vendure GraphQL schema with custom types, queries, mutations, and resolvers. Handles RequestContext threading, permissions, and dual Shop/Admin API separation. Use when adding GraphQL endpoints to Vendure.
vendure-plugin-reviewing
by merileyReview Vendure plugins for best practices violations. Detects missing decorators, improper DI, hardcoded config, lifecycle hook issues, and service anti-patterns. Use when reviewing plugin PRs or auditing plugin quality.
vendure-plugin-writing
by merileyCreate production-ready Vendure plugins with @VendurePlugin decorator, NestJS dependency injection, lifecycle hooks, and configuration patterns. Use when developing new Vendure plugins or extending existing ones.
vendure-admin-ui-reviewing
by merileyReview Vendure Admin UI extensions for React pattern violations, missing hooks, improper state management, and UI anti-patterns. Use when reviewing Admin UI PRs or auditing UI quality.
vendure-delivery-plugin
by merileyBuild delivery and fulfillment plugins for Vendure with idempotency, capacity management, timezone handling, and N+1 prevention. Covers ShippingCalculator, FulfillmentHandler, and slot reservation patterns. Use when implementing delivery features.
vendure-admin-ui-writing
by merileyCreate Vendure Admin UI extensions with React components, route registration, navigation menus, and GraphQL integration. Handles useQuery, useMutation, useInjector patterns. Use when building Admin UI features for Vendure plugins.
vendure-developing
by merileyDevelop Vendure e-commerce plugins, extend GraphQL APIs, create Admin UI components, and define database entities. Use vendure-expert agent for comprehensive guidance across all Vendure development domains.
vendure-entity-reviewing
by merileyReview Vendure entities for missing VendureEntity inheritance, improper decorators, missing migrations, index violations, and TypeORM anti-patterns. Use when reviewing entity PRs or auditing data models.
vendure-entity-writing
by merileyDefine Vendure database entities extending VendureEntity, with TypeORM decorators, relations, custom fields, and channel-awareness. Use when creating database models in Vendure.
vendure-graphql-reviewing
by merileyReview Vendure GraphQL resolvers for missing RequestContext, improper permissions, DTO violations, and schema extension issues. Use when reviewing GraphQL PRs or auditing API quality.
pr-description-writer
by merileyWrites and verifies GitHub pull request descriptions with zero fabrication tolerance. Discovers project PR templates, generates descriptions from git changes, and applies technical documentation verification standards. Use when creating PR descriptions (automatically invoked by create-pr skill), verifying PR descriptions, updating PR descriptions after additional changes, discovering templates, or when user requests "write PR description" or "verify PR description".
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.