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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daily-followup-drafts
by miriamdongDraft follow-up emails for today's completed sales calls using transcripts and CRM context
pipeline-gap-check
by miriamdongScans HubSpot CRM deals, Gmail inbox, and Google Calendar to surface open deals that are missing next steps and flags urgent follow-ups the rep needs to take action on. Use this skill whenever the user asks to check their pipeline, scan their deals, find missing next steps, or get a daily/morning pipeline briefing. Trigger phrases include: "check my pipeline", "what deals need attention", "scan my CRM", "pipeline gap check", "which deals don't have next steps", "what am I missing in my deals", "morning pipeline check", "what should I follow up on today", or any request that combines CRM + inbox scanning to prioritize sales actions. Also trigger proactively when the user asks what they should work on today from a sales perspective.
post-call-deal-creator
by miriamdongAfter sales calls, automatically check Fireflies transcripts for Miriam's completed calls, identify any companies that don't yet have a HubSpot deal, create those deals with correct fields pre-filled, and add a rich qualification note to every deal. Use this skill whenever the user says "create deals from today's calls", "log deals from my calls", "which calls didn't get deals", "add deals to HubSpot", "update HubSpot after my calls", "post-call deal sync", or "log that call". Also trigger automatically when invoked after daily-followup-drafts completes. If the user mentions they just finished a call and need to log it, or says "create a deal for [company]", use this skill. Do NOT wait for the user to explicitly say "skill" — whenever the context is post-call CRM logging, use this skill.
stalled-deal-nudge
by miriamdongDraft re-engagement nudge emails for deals silent 7+ days after a substantive call. Creates Gmail drafts only — never auto-sends. Chains off friday-pipeline-gap-check.
prospect-deep-dive
by miriamdongBuild a comprehensive multi-dimensional prospect deep-dive on a target company plus key contact — industry, product, ICP, business model, positioning, history, funding, investors, marketing, growth, competitors, stakeholders, LinkedIn, and AEO/GEO answer-engine visibility — into one Notion page with sales-ready Virio outreach angles. Use this skill whenever the user says "do a deep dive on [company]", "comprehensive analysis of [company]", "research [company] for me", "tell me everything about [company]", "build a profile on [company]", "account research on [company]", "full prospect workup on [name/domain]", or pastes a company URL plus a contact name/email and asks for analysis spanning more than one dimension. Trigger proactively when the request is broad and synthesis-oriented. For narrower asks use linkedin-content-analyzer (LinkedIn only), linkedin-competitor-benchmark (2+ competitors), or prospect-deck (post-discovery 2nd-call pitch deck).
event-attendee-icp-scanner
by miriamdongGiven an upcoming event, pull the attendee list, score each person against Virio's ICP criteria, and produce a ranked Notion page showing who to prioritize — with LinkedIn context, a personalized conversation opener, and pre-event warm-up messages for top targets. Use this skill whenever the user says "prep me for [event]", "who should I talk to at [event]", "scan attendees for [event]", "score the [event] attendee list", "who's going to [event]", or shares a Luma URL and asks who to meet. Also use this skill proactively whenever an event URL appears in conversation context and Miriam seems to be attending — don't wait for an explicit request.
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.