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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wordpress-content
by GuillaumeBldCreate and manage WordPress posts, pages, media, categories, and menus. Workflow: determine content type, choose method (WP-CLI or REST API), execute, verify. Use when creating blog posts, updating pages, uploading media, managing categories and tags, updating menus, or doing bulk content operations on WordPress sites.
web-asset-generator
by GuillaumeBldGenerates web assets like favicons, app icons, and social media images for web applications. Use when creating favicons, generating app icons, creating social media preview images, optimizing web assets, or preparing assets for deployment. Based on alonw0/web-asset-generator.
ffuf-web-fuzzing
by GuillaumeBldExpert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis. Use when performing web application security testing, fuzzing endpoints, testing authentication mechanisms, or conducting penetration tests. Based on jthack/ffuf_claude_skill.
vite-flare-starter
by GuillaumeBldScaffold a full-stack Cloudflare app from vite-flare-starter — React 19, Hono, D1+Drizzle, better-auth, Tailwind v4+shadcn/ui, TanStack Query, R2, Workers AI. Run setup.sh to clone, configure, and deploy.
playwright-skill
by GuillaumeBldGeneral-purpose browser automation using Playwright for UI verification, testing, and debugging web applications. Use when testing web applications, automating browser interactions, verifying UI elements, debugging frontend issues, or performing end-to-end testing. Based on lackeyjb/playwright-skill.
ios-simulator-skill
by GuillaumeBldiOS app building, navigation, and testing through automation. Uses semantic navigation on accessibility APIs to interact with iOS simulators by meaning, not pixel coordinates. Includes 21 production scripts for building, testing, navigation, accessibility audits, visual testing, and device lifecycle management. Works across different screen sizes and survives UI redesigns. Token optimized (96% reduction vs raw tools). Use when building iOS apps, testing iOS applications, automating iOS simulator interactions, or performing accessibility audits on iOS apps. Based on conorluddy/ios-simulator-skill.
loki-mode
by GuillaumeBldMulti-agent autonomous startup system - orchestrates 37 AI agents across 6 swarms to build, deploy, and operate a complete startup from PRD to revenue. Use when building complete startup systems, orchestrating multiple AI agents, automating full-stack development, or managing complex multi-agent workflows. Based on asklokesh/claudeskill-loki-mode.
skills-store-access
by GuillaumeBldMAIN ENTRY POINT - Equip any AI system (Claude Code, Claude.ai, Claude API, Cursor, or any platform) with complete Skills store capabilities. This foundational skill enables intelligent skill discovery, proactive installation, and lifecycle management without filling context. Works with any system supporting Claude skills - platform-aware installation auto-detects environment and uses appropriate method. Once equipped, create, store, fetch, search, discover, and manage skills in the Skills_librairie repository. Includes lightweight discovery system (90%+ context reduction), intelligent task analysis, proactive installation for ongoing projects, and complete cross-platform compatibility. Use when you need to equip any AI system with Skills store access, discover relevant skills for tasks, install skills proactively, or manage skills across platforms. The Skills store is located at https://github.com/GuillaumeBld/Skills_librairie and contains organized skills by category in the Skills/ directory.
ralph-loki
by GuillaumeBldAutonomous full-stack startup builder combining Ralph's iterative PRD loop (snarktank/ralph) with Loki's multi-agent swarm orchestration (loki-mode). Use when building complete applications or startups end-to-end: PRD → user stories → autonomous parallel agent execution → deployed product. Triggers on: build this app, execute this PRD, autonomous build, ralph-loki, launch this startup, full-stack autonomous, implement end-to-end. Combines: Ralph (iterative agent loop, prd.json, progress.txt), Loki (37 agents, 6 swarms), feature-forge (requirements), fullstack-guardian (cross-stack implementation), senior-fullstack (scaffolding).
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.