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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yuv-viral-video
by hoodiniEdit any selfie or screen-share footage into a viral short-form video in YUV.AI's signature style — Apple-style liquid-glass cards (real CSS backdrop-filter), dark-mode polish, MrBeast-paced cuts, video-title karaoke captions, premium GSAP motion graphics, no fake content, never covering the speaker's face. Hebrew is rendered in Rubik Black, English in Anton uppercase. Always renders BOTH 9:16 and 16:9 and always saves with _V<N> suffix for backups. Trigger when the user drops a path to an .mp4/.mov/.mkv and says "edit this", "make it viral", "turn this into a short", or any Hebrew equivalent (ערוך סרטון, סרטון ויראלי, להפוך לוויראלי, ריל, שורט). The pipeline is the COMBINATION of two skills: video-use (transcription + word-snapped cuts + base extraction) and hyperframes (HTML/CSS/GSAP visual composition + render). Do NOT use for podcast-only audio edits.
yuv-pilot
by hoodiniTop-of-pyramid orchestrator for Yuval Avidani's YUV.AI brand work. Apply when (a) the user wants YUV.AI output and the medium is ambiguous or multi-medium, (b) the user is planning a launch or cross-channel campaign for YUV.AI, or (c) explicitly invokes /yuv-pilot or asks "what should I build for YUV.AI / my brand". Triggers: "for YUV.AI", "for my brand", "YUV.AI launch", "ship something for me", "orchestrate", "cross-channel", "multi-platform for me", "yuv-pilot", Hebrew השקה, מולטי-פלטפורמה ל-יובל. Does NOT do the work — it identifies the right downstream YUV.AI skills (yuv-design-system across 3 modes, yuv-decks for slides, yuv-viral-video for short MP4s, hyperframes for HTML→MP4, nano-banana for in-brand imagery, gsap for animation), explains the composition, hands off. Does NOT apply to non-YUV.AI requests. When a request is clearly single-medium (just a deck, just a viral short), the specific skill wins — yuv-pilot is the front door for ambiguous or multi-output requests.
yuv-design-system
by hoodiniYuval Avidani's YUV.AI brand and design system. Apply ONLY when YUV.AI-branded output is requested — presentations, decks, keynotes, portfolio, brand site, profile, speaker bio, brand assets, or any prompt mentioning YUV.AI / "my brand" / "my deck" / "my site" / "for me". Do NOT auto-trigger on generic "build a game / web app / dashboard / landing page" without YUV.AI context — those use whatever palette fits. Three modes; NEON (hot pink
yuv-decks
by hoodiniBuild cinematic, narrative-driven presentation decks in Yuval Avidani's signature style using @open-slide/core. The user describes a topic and audience; this skill scaffolds an open-slide project, drafts the 4-act narrative arc (Boarding → Ascent → Cruise → Descent), writes every slide in the Yuval voice (plain-language, no jargon, story-driven), applies the brand visual language from yuv-design-system, and orchestrates companion skills for hero images and video moments. Triggers on "make a deck", "create slides", "build a presentation", "build a deck", "new deck", "presentation about", "talk deck", "hackathon deck", "open-slide deck", "yuv-decks", "yuv deck", "deck like Yuval", "מצגת", "שקפים", "דק", "מצגת על", "להכין מצגת". Use proactively whenever the user asks for ANY slide-based talk; the skill self-selects the right scope.
x-twitter-scraper
by hoodiniX (Twitter) data extraction and scraping. Use when asked to scrape tweets, extract followers, search Twitter/X users, download media from tweets, monitor X accounts, or analyze Twitter engagement. Triggers on twitter, x.com, tweet, follower, following, retweet, quote tweet, scrape, OSINT.
mongodb
by hoodiniWork with MongoDB databases using best practices. Use when designing schemas, writing queries, building aggregation pipelines, or optimizing performance. Triggers on MongoDB, Mongoose, NoSQL, aggregation pipeline, document database, MongoDB Atlas.
aws-strands
by hoodiniBuild AI agents with Strands Agents SDK. Use when developing model-agnostic agents, implementing ReAct patterns, creating multi-agent systems, or building production agents on AWS. Triggers on Strands, Strands SDK, model-agnostic agent, ReAct agent.
cloudflare
by hoodiniBuild and deploy on Cloudflare's edge platform. Use when creating Workers, Pages, D1 databases, R2 storage, AI inference, or KV storage. Triggers on Cloudflare, Workers, Cloudflare Pages, D1, R2, KV, Cloudflare AI, Durable Objects, edge computing.
owasp-security
by hoodiniImplement secure coding practices following OWASP Top 10. Use when preventing security vulnerabilities, implementing authentication, securing APIs, or conducting security reviews. Triggers on OWASP, security, XSS, SQL injection, CSRF, authentication security, secure coding, vulnerability.
railway
by hoodiniDeploy applications on Railway platform. Use when deploying containerized apps, setting up databases, configuring private networking, or managing Railway projects. Triggers on Railway, railway.app, deploy container, Railway database.
github-trending
by hoodiniFetch and display GitHub trending repositories and developers. Use when building dashboards showing trending repos, discovering popular projects, or tracking GitHub trends. Triggers on GitHub trending, trending repos, popular repositories, GitHub discover.
analytics-metrics
by hoodiniBuild data visualization and analytics dashboards. Use when creating charts, KPI displays, metrics dashboards, or data visualization components. Triggers on analytics, dashboard, charts, metrics, KPI, data visualization, Recharts.
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.