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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content-reflection
by drshailesh88Pre-publication quality assurance for cardiology thought leadership content. Use AFTER any content is drafted to evaluate scientific rigor, voice authenticity, positioning alignment, audience calibration, and credibility risk. Provides structured critique with specific revision suggestions. Works on any content type—tweets, threads, newsletters, editorials, video scripts.
cardiology-editorial
by drshailesh88Comprehensive cardiology editorial writing system for thought leadership newsletters. Use when the user wants to: (1) Identify and score recent landmark trials from top cardiology journals (NEJM, JAMA, Lancet, JACC, EHJ, etc.), (2) Write evidence-based editorials in Eric Topol's style from Ground Truth, (3) Create 500-word commentaries on clinical trials with PubMed citations, (4) Analyze trial importance using hybrid rules + LLM scoring, (5) Write editorials from full papers OR abstract-only scenarios, (6) Build thought leadership content for cardiologists, or (7) Synthesize recent cardiology advances for peers and referring physicians.
cardiology-newsletter-writer
by drshailesh88Create evidence-based cardiology newsletters for thought leadership in Eric Topol's authoritative Ground Truths style. Use when the user wants to analyze trending medical topics with engagement predictions, conduct data-driven topic selection, research medical literature using PubMed, or write comprehensive well-referenced newsletters that build professional authority as an interventional cardiologist. Handles complete workflow from trend analysis to final draft with smooth analytical flow between topics.
deep-researcher
by drshailesh88Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources. Uses file-based research tracking, parallel investigation threads, and context-efficient patterns for deep investigations. ALL MEDICAL CITATIONS FROM PUBMED MCP ONLY.
medical-newsletter-writer
by drshailesh88Create evidence-based medical newsletters for interventional cardiologists in Eric Topol's authoritative Ground Truths style. Use when the user wants to analyze trending medical topics with engagement predictions, conduct data-driven topic selection, research medical literature using PubMed, or write comprehensive well-referenced newsletters that build professional authority. Handles complete workflow from trend analysis to final draft.
parallel-literature-search
by drshailesh88Parallel search across PubMed, Perplexity, and your knowledge base. Searches all sources simultaneously and synthesizes findings with citations. Faster evidence gathering for clinical questions.
quick-topic-researcher
by drshailesh88Rapid topic mastery for video/content prep. Takes a topic → generates 5 research questions → parallel PubMed + web search → outputs McKinsey-style brief in 5 minutes. Use BEFORE recording videos or writing content.
g2-charts
by drshailesh88Declarative, grammar-based charting using AntV G2 for complex medical visualizations. Use when creating forest plots, Kaplan-Meier curves, funnel plots, waterfall charts, or any statistical/clinical chart that requires grammar-of-graphics composition with medical-journal-grade styling and accessibility-validated color palettes.
cardiology-science-for-people
by drshailesh88Write rigorous, accurate cardiology science for general audiences—not doctors. Use when the user wants to: (1) Explain clinical trials or research in plain English, (2) Write science content an 8th grader can understand WITHOUT dumbing it down, (3) Create thought leadership for the intelligent public rather than medical peers, (4) Transform complex cardiology findings into stories and narratives, (5) Write pieces where readers DON'T need another LLM to understand the explanation. Maintains full scientific rigor with PubMed citations for verification while avoiding academic language, trial acronyms, and intimidating statistics.
academic-chapter-writer
by drshailesh88Comprehensive academic textbook chapter writing system for medical/scientific content. Use when the user wants to: (1) Write a full textbook chapter (5,000-15,000 words) on any medical/scientific topic, (2) Generate a detailed table of contents with section word counts, (3) Research topics via PubMed MCP and compile 20-30 references, (4) Write section-by-section with proper citations in Vancouver format, (5) Create publishable academic content with Eric Topol-inspired voice and authentic human prose, (6) Get approval at TOC stage before writing begins, (7) Export well-structured chapters for textbook publication.
video-delivery-coach
by drshailesh88Analyze YOUR video recordings before publishing. Evaluates voice (pace, pitch, volume), facial expressions (emotions, eye contact, smiles), and content (filler words, structure). Helps improve your Hinglish YouTube delivery over time.
cremieux-cardio
by drshailesh88Write data-driven, evidence-first long-form Twitter posts on medicine and cardiology. Use when the user wants to: (1) Create thought leadership content in the style of Eric Topol, Peter Attia, Andrew Huberman, or Rhonda Patrick, (2) Present clinical evidence with charts, data, and Q1 journal citations for educated non-specialist audiences, (3) Write confident, matter-of-fact medical content that is rigorous without being inaccessible, (4) Explain trials, drugs, or medical phenomena using data visualization and systematic evidence review, (5) Build authority through methodological rigor and clear conclusions backed by evidence. NOT for newsletters or Substack. For Twitter long-form posts only.
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.