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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andrew-kane-gem-writer
by Mick-P-UKThis skill should be used when writing Ruby gems following Andrew Kane's proven patterns and philosophy. It applies when creating new Ruby gems, refactoring existing gems, designing gem APIs, or when clean, minimal, production-ready Ruby library code is needed. Triggers on requests like "create a gem", "write a Ruby library", "design a gem API", or mentions of Andrew Kane's style.
week-review
by Mick-P-UKReview week's progress with concrete accomplishments (not fake percentages), pattern detection, and goal tracking.
commitment-scan
by Mick-P-UKScan ScreenPipe data for uncommitted asks and promises, match to projects/people, and offer to create tasks.
screenpipe-setup
by Mick-P-UKEnable ambient work intelligence via screen OCR (beta feature)
integrate-mcp
by Mick-P-UKIntegrate existing MCP servers from Smithery.ai or GitHub repositories
thumbnail-play-button
by Mick-P-UKAdd a YouTube-style play button overlay to any image thumbnail. Use this skill whenever the user uploads an image and wants to make it look like a clickable video link - for newsletters, websites, WordPress posts, or any document where a thumbnail should indicate a video. Triggers include "add a play button", "make this look clickable", "add a video overlay", "YouTube style", or any request to overlay a play button on a thumbnail image.
review
by Mick-P-UKEnd of day review with learning capture. Integrates with evening journaling if enabled.
dex-obsidian-setup
by Mick-P-UKEnable Obsidian integration and migrate existing vault to wiki links
nina-to-notion
by Mick-P-UKPosts a completed Nina financial analysis report to the Notion Research Database. Reads the Nina .md report file, generates a structured summary (180-200 words, section headings + bullet points), creates a correctly titled and tagged page in the Research Database, populates the EPIC relation field, and writes the summary to the Summary (item) property. Use this skill whenever Mick says "post Nina's report to Notion", "log this to Notion", "add to the research database", or when offered automatically at the end of a sharescope-nlm-research run.
pns
by Mick-P-UKPost Notion Summary. When invoked on a Notion page containing an announcement, article, or report, reads the page content, generates a concise structured summary (max 200 words), and posts it into the Summary (item) property field. Adapts section headings to content type (financial results, trading updates, general articles). Use on any Notion database page with a Summary (item) text property.
ai4inv-webinar-processor
by Mick-P-UKProcesses a monthly "AI for Investors" (ai4inv) webinar recording into a full NotebookLM source, a formatted Word user guide, and an updated notebook index. Use this skill whenever Mick asks to "process the [month] webinar", "add the [month] webinar to NotebookLM", "create the user guide for [month]", "do the webinar workflow for [month]", or any request to run the monthly webinar pipeline for the AI for Investors series. Also triggers on "run the webinar skill" or "do the webinar processor for [month]". Always use this skill for the webinar workflow - do not attempt it manually without reading it first.
resume-builder
by Mick-P-UKBuild resume and LinkedIn profile through guided interview
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.