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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product-spec-brainstorming
by scanadyTurn a vague idea into a fully-formed, approved design before any implementation begins. Use when the user wants to design a feature, plan what to build, explore approaches before coding, brainstorm a new component or system, think through requirements, or needs to understand constraints before committing to an implementation. Triggers: 'brainstorm', 'design this', 'help me think through', 'plan this feature', 'what should I build', 'let's think about this', 'design a system', 'explore approaches', 'before we code', 'requirements for'.
frontend-design-impactful
by scanadyDesign and build conceptually distinctive, award-caliber marketing surfaces that drive conversion and earn attention. Use when a site needs genuine uniqueness: "feels flat", "looks AI-generated", "needs to impress top designers", "award-worthy design", "standout landing page", "unlike any competitor". Two modes: conversion-safe for B2B precision, expressive for award ambition. Never generic, never templated.
policyholder-service
by scanadyBest practices for servicing life insurance policyholder requests. Use when handling customer inquiries, processing service transactions, or communicating with policyholders and agents. Covers call handling, verification, communication standards, and common service scenarios.
product-knowledge
by scanadyLife insurance product knowledge covering whole life, term life, universal life, variable universal life, indexed universal life, and joint survivorship products. Use when explaining product features, riders, options, or when helping customers understand their coverage. Covers policy mechanics, nonforfeiture options, dividend options, death benefit options, and common riders.
prompt-optimizer
by scanadyAnalyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.
research-deep-reading-analyst
by scanadyComprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'deep read,' 'use [framework name],' 'research-deep-reading-analyst,' or when users provide URLs/long-form content for analysis.
comms-engage-internal-community
by scanadyDraft internal community communications such as 3P updates, newsletters, FAQ responses, and team status reports. Use when you need clear, professional, and audience-aware internal messaging.
tech-arch-architecture-decision-records
by scanadyCreate, manage, and maintain Architecture Decision Records (ADRs). Use when making significant architectural decisions, documenting technology choices, recording design trade-offs, onboarding engineers to historical decisions, or establishing ADR processes. Invoke for: ADR writing, ADR templates, MADR format, decision documentation, architecture decision record, technical decision log, ADR lifecycle, supersede decision, deprecation ADR, RFC-style decision.
sales-pipeline-revops
by scanadyUse when user wants help with revenue operations, lead lifecycle, MQL/SQL definitions, lead scoring, lead routing, pipeline stages, deal desk, CRM automation, or data hygiene. Triggers: "RevOps", "revenue operations", "lead scoring", "lead routing", "MQL", "SQL", "pipeline stages", "deal desk", "CRM automation", "marketing-to-sales handoff", "speed-to-lead", "leads not reaching sales", "pipeline health", "CRM workflows", "lead qualification", "when should marketing hand off to sales".
strategy-frameworks-mckinsey-brief
by scanadyBuild executive-ready, board-level strategic problem-solving briefs using SCQ, MECE issue trees, hypothesis-driven analysis, and pyramid-structured recommendations. Use when asked for McKinsey-style strategy memos, root-cause diagnostics, turnaround plans, market-entry decisions, profitability fixes, steering committee briefs, or implementation roadmaps for complex business problems.
marketing-seo-cro
by scanadyAnalyzes landing pages and provides detailed CRO (Conversion Rate Optimization) recommendations. Use when user provides a landing page URL or HTML/CSS code and needs optimization advice to maximize conversions, signups, or sales. Extracts page elements, audits against proven CRO principles, and delivers actionable recommendations in report format.
data-science-autoresearch
by scanadyDesign autonomous AI research systems inspired by Karpathy's autoresearch framework. Use when asked to 'set up autoresearch', 'design an autonomous training loop', 'create an AI research experiment', 'build a self-improving model pipeline', 'autonomous model training', 'autoresearch for my problem', or when the user has a problem/challenge that could benefit from autonomous iterative model training with automated evaluation. Also use when asked to 'design evaluation criteria for model training', 'create a training harness', 'set up experiment tracking for ML', or when someone wants an AI agent to autonomously explore model architectures, hyperparameters, or training strategies to solve a specific problem.
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.