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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qa-generator
by meganopusGenerate concise, risk-focused test scenarios for features. Prioritizes critical paths and high-risk edge cases over exhaustive coverage. Use when the user needs a test plan, QA strategy, or verify acceptance criteria. Triggers on requests like "create test scenarios", "write QA plan", "how do I test this", or "generate test cases".
fsd-generator
by meganopusGenerate comprehensive Functional Specification Documents (FSDs) that translate PRD requirements into implementation-ready specifications. Use when the user needs to create an FSD, functional spec, system specification, or detailed feature specification. Triggers on requests like "create an FSD", "write functional specifications", "translate this PRD into specs", or any request to define system behaviors, data requirements, business rules, and acceptance criteria from a PRD or feature description.
design-system-generator
by meganopusCreate comprehensive design system documentation including components, design tokens, and UI patterns. Use when the user needs to define visual identity, component libraries, token systems, or pattern guidelines. Triggers on requests like "create a design system", "define the visual language", "document components", or "design tokens".
api-contract-generator
by meganopusGenerate comprehensive OpenAPI 3.0+ specifications from FSDs and ERDs. Use when the user needs a contract-first API design, Swagger documentation, or interface definitions for frontend/backend alignment. Triggers on requests like "create API contract", "generate OpenAPI spec", "design the API", or "Swagger definition".
wireframe-generator
by meganopusGenerate comprehensive UI/UX wireframes with component specifications, API interactions, and responsive behavior notes. Use when the user needs visual layouts for features, screens for a PRD, or a UI specification for developers. Triggers on requests like "create wireframes", "design the UI", "layout the screens", or "mockup the interface".
dependency-graph-manager
by meganopusUpdates the Global Dependency Graph (`docs/dependency-graph.md`) by scanning all feature stories for dependency metadata. Use this skill at the end of feature planning or whenever new stories are added/modified to keep the project-wide build order visualization up to date.
erd-generator
by meganopusGenerate Entity Relationship Diagrams from functional specs, PRDs, or user descriptions. Use when the user needs a data model, database schema design, or entity-relationship mapping. Triggers on requests like "create an ERD", "design the database", "data model for this feature", or "entity relationships".
prd-generator
by meganopusGenerate comprehensive Product Requirements Documents (PRDs) that serve as the single source of truth for engineering, design, QA, and stakeholders. Use when the user needs to create a PRD, feature specification, product requirements, or feature requirements document. Triggers on requests like "create a PRD", "write product requirements", "document this feature", or any request to define a product or feature's purpose, scope, user stories, and success criteria.
product-brief-generator
by meganopusGenerate polished, executive-level Product Briefs (Executive Summaries) that communicate a product's essence to stakeholders. Use when the user needs to create a product brief, executive summary, product overview, investor-ready product document, or stakeholder alignment document. Triggers on requests like "create a product brief", "generate an executive summary for my product", "write a product overview", or any request to summarize a product's value proposition, capabilities, and positioning in a structured format.
story-generator
by meganopusDecompose features, epics, or technical designs into granular, implementation-ready User Stories (PRODUCT-CODE-XXX). Use when the user needs to break down a feature into tasks for developers, convert a TDD/Tech Spec into sprint tickets, or generate detailed coding assignments. Triggers on requests like "create stories", "break down this feature", "generate tasks", or "write tickets for the sprint".
tdd-generator
by meganopusGenerate a Technical Design Document (TDD) or lightweight Tech Spec for a feature. Supports LIGHT mode (~3-page spec for simple changes) and HEAVY mode (full architecture document for complex features). Use when the user needs technical implementation specs, architecture decisions for a feature, or a developer-facing design document. Triggers on requests like "create a TDD", "write a tech spec", "technical design for this feature", or "architecture document".
tech-stack-architecture-decision
by meganopusDefine and document the technology stack and architecture decisions for a project. Use when the user needs to choose a tech stack, make architecture decisions, define infrastructure choices, or document technology selections. Triggers on requests like "define the tech stack", "choose technologies", "architecture decisions", "what stack should we use", or any request to select and document frontend, backend, database, auth, hosting, and infrastructure choices for a project.
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.