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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fashion-editorial-prompter
by SheshiyerGenerate AI image prompts for high-fashion editorial photography, apparel lookbooks, sport jerseys, cross-brand fashion collaborations, and identity-preserving pose grids. This skill should be used when creating fashion campaigns, editorial shoots, branded apparel visualizations, or multi-angle contact sheets with JSON-structured identity anchoring.
illustration-style-prompter
by SheshiyerGenerate AI prompts for non-photorealistic art including posters, Notion-style icons, sticker packs, anime characters, woodblock/ukiyo-e prints, Swiss Design typographic posters, and custom typography art. Use when creating illustrations, character designs, branded icon sets, or artistic style explorations with [BRAND NAME] or [SUBJECT] substitution.
agent-ui
by SheshiyerBatteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent, agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop
brand-product-prompter
by SheshiyerGenerate AI image prompts for branded product concepts, packaging mockups, luxury reinterpretations, and product visualizations. This skill should be used when creating speculative brand products, souvenir collections, mockups (duffle bags, sneakers, passport covers), or dieline-to-3D packaging renders with [BRAND NAME] substitution.
product-photography-prompter
by SheshiyerGenerate AI prompts for commercial product photography including beverage hero shots, cosmetic flat-lays, and design catalog layouts with technical specification panels. Use when photographing REAL products (not speculative concepts) with JSON-structured prompts optimized for Nano Banana Pro and GPT Image.
visual-asset-prompt-generator
by SheshiyerReads brand documentation (visual identity, product description, positioning) and generates optimized prompts for image generation (Nano Banana/Flux via fal.ai) and 3D model generation (Hunyuan3D). Use when generating visual assets from brand docs.
supacode-cli
by SheshiyerControl Supacode from the terminal. Use when running Supacode CLI commands, managing worktrees, tabs, and surfaces programmatically, or when inside a Supacode terminal session.
flox-environments
by SheshiyerCreate reproducible, cross-platform development environments with Flox — a declarative environment manager built on Nix. ALWAYS use this skill when the user needs to: set up a project with system-level dependencies (compilers, databases, native libraries like openssl, libvips, BLAS, LAPACK); configure reproducible toolchains for Python, Node.js, Rust, Go, C/C++, Java, Ruby, Elixir, PHP, or any language; manage environments that must work identically across macOS and Linux; pin exact package versions for a team; run local services (PostgreSQL, Redis, Kafka) alongside development tools; onboard new developers with a single command; or solve 'works on my machine' problems. Especially valuable for AI-assisted and vibe coding — Flox lets agents install tools into a project-scoped environment without sudo, system pollution, or sandbox restrictions, and the resulting environment is committed to the repo so anyone can reproduce it instantly. Use this skill even if the user doesn't mention Flox — if they describe need
hugging-face-gradio
by SheshiyerBuild or edit Gradio apps, layouts, components, and chat interfaces in Python for ML demos and UI prototypes. USE WHEN a user wants a Gradio demo or Python ML interface for a model or agent.
superdesign
by SheshiyerFrontend UI/UX design agent for finding design inspiration and iterating design drafts on an infinite canvas, with CLI commands for projects, drafts, variations, and flow pages. USE WHEN designing a feature, page, or flow before implementation, exploring style variations, or reproducing an existing UI.
raycast-ai-extensions
by SheshiyerBuild Raycast AI extensions — expose tools that Raycast AI can call to read and act on behalf of the user. Covers the tools[] manifest, tool function shape, AI instructions, confirmations for side-effecting tools, and evals. USE WHEN making an extension AI-callable or building tools for Raycast AI. Defer exact API to developers.raycast.com/ai.
raycast-orchestrator
by SheshiyerRoute a Raycast task to the right spoke — build an extension/command, build an AI extension (tools the Raycast AI calls), publish to the Store, or design a Raycast-aesthetic UI in your own app. USE WHEN a user wants to work with Raycast but the specific concern isn't named.
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.