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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qms-audit-expert
by borgheiISO 13485 internal audit expertise for medical device QMS. Covers audit planning, execution, nonconformity classification, and CAPA verification. Use for internal audit planning, audit execution, finding classification, external audit preparation, or audit program management.
qa-browser-automation
by borgheiUse when performing browser-based QA testing, visual regression tracking, WCAG accessibility auditing, performance profiling, or health scoring web applications. Combines Chrome MCP browser control with Python analysis tools for systematic, repeatable quality assurance.
xlsx-toolkit
by borgheiAudit Microsoft Excel (.xlsx) workbooks for sheet count, cell count, formula density, external references, named ranges, hidden sheets, and data validation rules. Use when reviewing a financial model, sharing a workbook externally, or when the user mentions xlsx audit, spreadsheet review, formula audit, or workbook leakage check.
mdr-745-specialist
by borgheiEU MDR 2017/745 compliance specialist for medical device classification, technical documentation, clinical evidence, and post-market surveillance. Covers Annex VIII classification rules, Annex II/III technical files, Annex XIV clinical evaluation, and EUDAMED integration.
lead-researcher
by borgheiQualify and prioritize sales leads against an ICP definition, score lead lists for outreach worthiness, and draft personalized outreach hooks. Use when building a target account list, qualifying inbound leads, prepping for outreach, or when the user mentions lead research, prospecting, sales outreach, ICP scoring, or account targeting.
deal-desk
by borgheiDeal desk: the cross-functional function that reviews, approves, and structures non-standard sales deals. Use when standing up a deal-desk function from scratch, defining the deal-desk charter and SLA, building approval-threshold matrices (discount %, contract length, custom terms, payment terms, custom SLAs), designing a deal-review packet template, routing deals through the right approvers, analyzing deal velocity to find bottlenecks, or auditing recent deals for policy compliance. Pairs with our existing pricing-strategy (sets the prices) and revenue-operations (measures the funnel) skills — this one runs the operational machinery between those two.
talent-acquisition
by borgheiExpert talent acquisition covering recruiting strategy, candidate sourcing, interview design, employer branding, and hiring analytics. Use when building job descriptions, designing interview scorecards, analyzing hiring funnels, drafting sourcing outreach, structuring compensation bands, or improving offer acceptance rates.
email-triage
by borgheiClassify a batch of email subjects/snippets into action categories (reply now / reply later / archive / delete / unsubscribe), and surface unsubscribe candidates and recurring senders. Use after a busy week, when running inbox-zero, or when the user mentions inbox triage, email overload, unsubscribe, or inbox zero.
ecommerce-advisor
by borgheiStrategic advisory for e-commerce founders covering unit economics, fulfillment models (3PL, DTC, dropship, retail), payment economics, returns / refunds, and channel strategy (DTC site / Amazon / wholesale / retail). Use when evaluating an ecommerce idea, modeling unit economics, picking fulfillment strategy, or when the user mentions DTC, e-commerce, Shopify, Amazon, 3PL, CAC, contribution margin, or retail expansion.
context-engine
by borgheiContext management engine for AI coding agents. Handles context window optimization, persistent memory across sessions, context retrieval strategies, token budget allocation, and knowledge graph construction from codebases. Use when building agent memory systems, optimizing context windows, designing RAG pipelines for code, or managing multi-session agent state.
paid-ads
by borgheiPlan, execute, and optimize paid advertising campaigns across Google Ads, Meta Ads, LinkedIn Ads, Twitter/X, and TikTok. Covers campaign structure, audience targeting, budget allocation, bid strategies, retargeting, attribution, and performance optimization. Use when running PPC campaigns, setting up ad accounts, optimizing ROAS/CPA, or when user mentions paid ads, PPC, Google Ads, Meta Ads, LinkedIn Ads, ad campaigns, retargeting, audience targeting, budget optimization, ROAS, CPA, or ad performance.
pitch-deck-reviewer
by borgheiScore a pitch deck (provided as text/markdown summary of slides) against YC, Sequoia, and a16z heuristics — does it have the right slides in the right order with the right content? Use before sending a deck to investors, practicing pitch with a co-founder, or when the user mentions pitch deck, fundraising deck, seed deck, or Series A deck review.
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.