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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workflow-creator
by ray-amjadAuthor runnable workflow scripts for Claude Code's Workflow tool — deterministic multi-agent orchestration files that fan out fresh-context subagents under plain JavaScript control flow. Use this skill whenever the user wants to create, write, build, scaffold, design, or fix a workflow: "make a workflow", "create a workflow for X", "write a workflow", "turn this into a workflow", "scaffold a multi-agent pipeline", "orchestrate this with subagents deterministically", or any request to author or edit a .js file under .claude/workflows/. Also use it when the user is confused about the workflow script format — the meta block, agent()/parallel()/ pipeline()/phase(), schemas, the determinism rules — or when a workflow errors and needs debugging. Trigger this even when the user only describes a repeatable multi-step or parallel job and seems to want it packaged as a workflow, even if they never say the word "workflow". Do NOT use it to merely run an existing workflow, or for a one-off single-subagent task.
wise-transfer-verify
by ray-amjadVerify whether a Wise transfer actually went through before re-sending it. Use this skill whenever Ray gets an email or message claiming a payment "didn't arrive", "wasn't transferred", "expired", "was cancelled", "got bounced back", or "please re-send" — especially affiliate payouts, vendor payments, or transfers Ray sent via Wise. Also trigger when Ray says "check if X got paid", "did this go through", "look up that transfer", "verify before I resend", or pastes a Wise reference like RW20260523 and wants its status. Talks to the live Wise API across every profile (personal, Ray Amjad Ltd, Project Solve Labs CIC, Intentional AI Ltd), not just the CSV statement, so it catches the real outcome (sent / refunded / cancelled / waiting). Always run this BEFORE sending a replacement payment so Ray doesn't double-pay someone who already got their money or who already has a pending re-send queued up.
next-up
by ray-amjadShow what to work on next by reading the Dashboard and following links to project boards and corrections. Use this skill whenever the user asks "what should I work on", "what's next", "show me my queue", "what's in progress", or wants a quick view of active tasks across all projects. Also triggers on /next-up.
class-script-writer
by ray-amjadTurn a raw idea, source tweet, video transcript, feature announcement, or rough outline into a polished video script for Ray's Agentic Coding School classes. Use this skill whenever the user says "write a class script", "turn this into a class video", "script this for the [business/claude-code/prompt-engineering/skills/context-engineering/techniques/claude-chat/claude-cowork/correction] class", "draft an agentic coding school video", or whenever the user is staging content in `/Users/ray/Desktop/ray-os/projects/agentic-coding-school/to-film/` and wants it written up. Default to this skill for any new file destined for the `to-film/` tree, even if the user does not name it explicitly. Produces an essay-style markdown script with the correct frontmatter, horizontal rules between sections, inline image embed placeholders, and a Demo section, written in Ray's direct second-person voice.
hackernews-monitor
by ray-amjadScout Hacker News for trending AI and developer discussions, launches, and debates that could become video content. Use this skill whenever the user wants to check Hacker News, find trending tech discussions, scout HN for video ideas, or says things like "what's on HN", "check Hacker News", "anything trending on HN", "scout Hacker News", or "what are devs talking about". Also triggers on "monitor layer", "input layer", or "fountainhead" when Hacker News is relevant.
youtube-outlier-titles
by ray-amjadBrowse any YouTube channel, identify outlier videos (3x+ above channel average views), and analyze their titles for patterns. Use this skill whenever the user wants to find outlier videos on a channel, analyze what titles are working for another creator, scout title ideas, or says things like "find outliers on this channel", "what's working for [creator]", "analyze their titles", "outlier analysis", "title research", "scout this channel", or provides a YouTube channel URL/handle and wants to understand their best-performing content. Also trigger when the user shares a channel page screenshot with view counts and wants analysis.
video-editor
by ray-amjadEdit raw screen recording videos with synced audio. Removes repeated takes, false starts, silence, filler, and [MUSIC] tags to produce a clean final cut. Use when the user provides a raw/unedited MP4 video file and wants it edited down. Triggers on: "edit this video", "cut this video", providing a raw MP4 for editing, or any request to remove bad takes from a recording.
sentence-mining
by ray-amjadBuild Japanese sentence-mining cards for Ray's Anki deck in two modes. (1) Video mode — paste any Instagram reel, YouTube video/Short, TikTok, Twitter video, or local file → yt-dlp + AssemblyAI + mecab + AnkiMorphs i+1 diff produces draft cards. (2) Bank mode — give a list of target words → search across Ray's locally-indexed subs2srs .apkg banks (Tokyo Ghoul, Harry Potter, LOGH, etc.) for natural example sentences, reusing the bank's original audio + screenshot when available. Both modes stage drafts for Ray's approval before pushing via AnkiConnect. Use proactively whenever input is (a) a Japanese-language video URL or (b) a list of Japanese words — don't ask if Ray wants cards, start drafting. Trigger phrases include "mine this video", "make sentence cards from <url>", "turn this reel into cards", "mine these words", "find sentences for [w1, w2, …]", "i keep forgetting <word>", "pull cards from my <show> bank", "leech these", "search the banks for X", `/sentence-mining`, or any video URL paired with a ment
linkedin-carousel
by ray-amjadGenerate LinkedIn carousel images for Ray Amjad in the Nate Herk / Twitter-card aesthetic — bold black headlines, light gray subtitles, profile chip with verified check, page counter, blue accent, final follow-CTA card. Renders square or 4:5 PNGs ready to upload as a multi-image LinkedIn post. Use this skill whenever the user wants to "make a linkedin carousel", "linkedin carousel about X", "/linkedin-carousel", "carousel for linkedin", "turn this into a linkedin carousel", "swipeable linkedin post", "linkedin slides", or asks to convert a video / X article / explainer / list into a multi-slide LinkedIn graphic. Also trigger when the user shares content (a script, transcript, blog post) and explicitly says they want it on LinkedIn as a carousel rather than as a text post.
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.