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.
Querying local SQLite index...
today
by bradautomatesGenerate daily plan from due tasks and active projects. Part of chief-of-staff vault system.
create-farmer
by bradautomatesCreate or schedule a context farmer. Checks existing farmers and offers to build new ones or schedule existing ones.
daily-review
by bradautomatesEnd of day review - compare planned vs actual, update task statuses. Part of chief-of-staff system.
delegate-task
by bradautomatesDelegate a task by forking a terminal session to a new terminal window. Use this when the user requests 'delegate' or 'create a new terminal' or 'new terminal: <command>' or 'fork session: <command>'.
farm
by bradautomatesManually trigger a context farmer subagent. Usage - /farm <name> (e.g., /farm slack)
new
by bradautomatesQuick capture - classify and file natural language input into the vault. Part of chief-of-staff system. Use for capturing tasks, ideas, project notes, or people notes.
start-second-brain
by bradautomatesInitialize a new Second Brain vault from a template repo — validate privacy, create folders, push, and onboard the user.
x-research
by bradautomatesResearch high-performing X/Twitter content from tracked accounts using Apify's Tweet Scraper V2. Identifies outlier tweets, trending topics, and content patterns to inform content strategy. Use when asked to: - Find trending tweets or content in a niche - Research what's performing on X/Twitter - Identify high-performing tweet patterns - Analyze competitors' X content - Generate content ideas from X trends - Run X/Twitter research Triggers: "x research", "twitter research", "find trending tweets", "analyze x accounts", "what's working on twitter", "content research x", "tweet analysis"
youtube-research
by bradautomatesResearch high-performing YouTube videos in a niche using TubeLab's outlier detection API. Identifies outlier videos, analyzes top 3 relevant videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending videos in a YouTube niche - Research competitor content - Discover viral video patterns - Generate content ideas based on what's working - Run YouTube research - Find outlier videos - Analyze hooks and content structure Triggers: "youtube research", "find outlier videos", "research YouTube trends", "what videos are performing well", "find content ideas for my channel", "youtube trends"
tiktok-research
by bradautomatesResearch high-performing TikTok videos from tracked accounts using Apify's TikTok Scraper. Identifies outlier content, analyzes top 5 videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending TikTok content in a niche - Research what's performing on TikTok - Identify high-performing video patterns - Analyze competitors' TikTok content - Generate content ideas from TikTok trends - Run TikTok research - Find viral TikToks - Analyze hooks and content structure Triggers: "tiktok research", "tt research", "find trending tiktoks", "analyze tiktok accounts", "what's working on tiktok", "content research tiktok", "tiktok analysis", "tiktok trends"
content-planner
by bradautomatesOrchestrate comprehensive content research across X, Instagram, YouTube, and TikTok platforms. Runs all research skills in parallel via subagents, then aggregates findings into actionable content plans and platform-specific intelligence playbooks. Use when asked to: - Create a content plan for social media - Research content across all platforms - Generate content ideas from multiple sources - Build a content strategy playbook - Aggregate research from X, Instagram, YouTube, TikTok - Run comprehensive content research - Create platform playbooks Triggers: "content plan", "content planner", "research all platforms", "comprehensive research", "content strategy", "multi-platform research", "create playbooks", "aggregate research"
instagram-research
by bradautomatesResearch high-performing Instagram content (posts and reels) from tracked accounts using Apify's Instagram Scraper. Identifies outlier content, analyzes top 5 videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending Instagram content in a niche - Research what's performing on Instagram - Identify high-performing reel patterns - Analyze competitors' Instagram content - Generate content ideas from Instagram trends - Run Instagram research - Find viral reels - Analyze hooks and content structure Triggers: "instagram research", "ig research", "find trending reels", "analyze instagram accounts", "what's working on instagram", "content research instagram", "reel analysis", "instagram trends"
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.