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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landing-page-gtm
by ooiyeefeiBuild and update high-converting SaaS landing pages with GTM-aware marketing copy, competitive positioning, and sales psychology. Use when creating new landing pages, rewriting feature cards, updating marketing copy, launching product pages, or transforming technical features into customer-facing sales language. Triggers on "build landing page", "update feature cards", "rewrite marketing copy", "create product page", "launch page", "GTM", "sales copy", "competitive positioning", or when converting product features into conversion-focused web pages.
mvp-launch
by ooiyeefeiWeb app MVP launch checklist knowledge. Auto-triggered when conversation involves "launch readiness", "MVP checklist", "production ready", "go live", or pre-launch verification. Provides the 10-point criteria for what counts as "done" in each area. Based on "Realistic MVP Launch Checklist (from building 30+ apps)".
product-management
by ooiyeefeiThis skill should be used when the user asks to "analyze my product", "research competitors", "find feature gaps", "create feature request", "prioritize backlog", "generate PRD", "plan roadmap", "what should we build next", "competitive analysis", "gap analysis", "sync issues", or mentions product management workflows. Provides AI-native PM capabilities for startups with signal-based feature tracking, the WINNING prioritization filter, and GitHub Issues integration with deduplication.
rethink-surveys
by ooiyeefeiDesign, critique, or scaffold surveys grounded in Caroline Jarrett, Dillman, and Tourangeau methods. Use when designing a new survey, critiquing an existing one, scoring or clustering responses, or turning questions into an app. Triggers on "survey", "questionnaire", "user research", "customer discovery", "intent capture", "interview script", "lint my survey", "score responses", or "how to ask better questions". When the bundled MCP server is connected, prefer its deterministic tools (`critique_survey`, `get_template`, `design_survey_session`, `score_response`, `cluster_responses`) over manual reasoning. Captures real past behavior over hypotheticals, supports text/voice/AI-interviewer modalities, and includes templates for event organizers, startup founders, and gig-economy workers.
agentic-system-design
by ooiyeefeiPrescriptive Q&A workflow for designing agentic pipelines, multi-model councils, sub-agent hierarchies, and tool-loop hardening for any domain. Use when the user asks to "design an agent", "design a multi-agent system", "should I use a council/debate", "build a [domain] review agent" (HAZOP, finance, tutorial, marketing, compliance, accounting), "real agency vs workflow", "how to add sub-agents", "AI for [domain] review", or names patterns like "orchestrator-worker", "evaluator-optimizer", "Magentic", "ReAct", "plan-and-execute", "handoffs". Walks the user through 12 stages one question at a time and emits a buildable design doc with citations. Do NOT use for general coding questions, single-shot prompt tuning, or bare "use Claude to do X" requests with no agency requirement.
excalidraw
by ooiyeefeiGenerate architecture diagrams as .excalidraw files from codebase analysis, with optional PNG/SVG export. Use when the user asks to create architecture diagrams, system diagrams, visualize codebase structure, generate excalidraw files, export excalidraw diagrams to PNG or SVG, or convert .excalidraw files to image formats.
htmldrop
by ooiyeefeiThis skill should be used when the user asks to "share this HTML", "publish HTML", "get a link for this file", "share this report", "make this shareable", "upload this HTML", or wants to publish any HTML artifact for others to view. ALSO use it for collaborative review on an HTML doc/spec/report — triggers include "get feedback on this", "let reviewers comment", "collect feedback", "share for review", "let people annotate this", "synthesize the feedback", "converge the feedback", "what did reviewers say", "incorporate the comments", or "improve this from the feedback". Wraps Surge.sh for zero-cost hosting with guided privacy options, plus an embedded annotation + AI converge workflow.
self-improving-systems
by ooiyeefeiDecide whether your agent actually needs persistent memory, feedback loops, or closed-loop learning, then design the smallest thing that pays for itself. Use when the user says "add memory", "give my agent context management", "make my agent learn", "self-improving / closed-loop", "Reflexion / mem0 / Letta / MemGPT", "AriGraph", "agent memory architecture", "long-term memory for chatbot", "why does my agent keep forgetting / making the same mistake", "fine-tune from agent traces", or asks for a memory schema / experience store / reward model. Filters ruthlessly — most teams want a state cache, not memory + learning. Default position is scratchpad-only with a stateless agent shipped first.
streak
by ooiyeefeiUniversal challenge tracker with flexible cadence, intelligent insights, and cross-challenge learning detection. Use when user wants to track any personal challenge - learning, habits, building, fitness, creative, or custom. Supports daily, weekly, or N-day check-ins with type-adaptive preferences, backlog, and context files.
uat-testing
by ooiyeefeiEnd-to-end User Acceptance Testing for web applications. Analyzes branch changes and specs to generate exhaustive test cases, sets up the local environment, executes tests via Playwright browser automation, and produces a pass/fail results report with screenshots and fix documentation. Use when the user says "run UAT", "test this feature", "UAT testing", "acceptance test", "test my branch", "generate test cases", or wants to verify a feature branch against its spec before merge.
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.