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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claude-reflect
by BayramAnnakovSelf-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
notebooklm-video-research
by BayramAnnakovAutomate NotebookLM notebook creation from YouTube videos. Given a YouTube video URL, extract information about people featured in the video, research them online, create a new NotebookLM notebook with the video and research as sources, and generate an audio overview. Uses screenshot-first automation to reliably target UI elements.
remotion-video-director
by BayramAnnakovInteractive guide for creating Remotion videos - from strategic concept to rendered MP4. Use when the user wants to create a video, make a Remotion project, build a product demo video, generate a launch video, make a recurring content template, create a marketing video, or says "I need a video for...", "help me make a video", "video for my product", "remotion video". Covers the full creative process: expert deliberation, scenario design, build, and review. Works alongside the official remotion-dev/skills for API-level guidance.
2026-coach
by BayramAnnakovExecutive coaching skill that helps you plan your 2026 using research-backed process goals. Guides you through discovery questions, creates outcome goals, converts them to daily behaviors, and sets up accountability systems. Use when you want to plan your year, set goals, or need a coach to help you stay focused.
synthetic-market-research
by BayramAnnakovFast, cheap market research using LLM-generated synthetic survey responses with Semantic Similarity Rating (SSR). Runs purchase intent, concept tests, and pricing research in minutes instead of weeks, at $0 per respondent. Based on PyMC Labs' validated methodology (90% correlation with real humans across 57 surveys). Triggers: synthetic research, market research, survey, purchase intent, concept test, consumer research, SSR, synthetic survey, product validation, pricing research, Likert scale.
agent-tower-plugin
by BayramAnnakovMulti-agent deliberation for Claude Code - orchestrate AI coding assistants (Claude, Codex, Gemini) for council, debate, and consensus workflows
chatgpt-app-builder
by BayramAnnakovBuild ChatGPT Apps using the Apps SDK and MCP. Use when users want to: (1) Evaluate if their product should become a ChatGPT App (2) Design and implement MCP servers with widgets (3) Test apps locally and in ChatGPT (4) Prepare for App Store submission Triggers: "ChatGPT app", "Apps SDK", "build for ChatGPT", "ChatGPT integration", "MCP server for ChatGPT", "submit to ChatGPT"
ideo-method-cards
by BayramAnnakovIDEO's 51 Design Method Cards for product design, user research, workshop planning, design sprints, and design thinking facilitation. Helps founders, PMs, and designers apply research methods interactively with smart recommendations, facilitation guides, and workshop planning. Triggers: design methods, user research, IDEO, design sprint, design thinking, method cards, facilitation, discovery sprint, empathy research, design research, user interviews, prototyping methods.
slide-inspector
by BayramAnnakovQuality audit for PowerPoint decks. Catches layout bugs, design inconsistencies, accessibility issues, AI-generation tells, and silent generator failures.
ai-personal-os-onboarding
by BayramAnnakovPersonalized onboarding for the AI Personal OS course. Runs a conversational interview to understand who you are, how you work, and what you want from the course — then creates your personal AI environment (CLAUDE.md, user-profile.md, course-goals.md, SOUL.md, achievements.md). Takes about 10-15 minutes. Use at the start of the course to set up your workspace.
telegram-assistant
by BayramAnnakovTelegram automation assistant using telegram-mcp. Use when users want to: (1) Get a digest of unread Telegram messages (2) Analyze their writing style from channel posts (3) Draft and publish posts to Telegram channels (4) Search and reply to messages across chats Triggers: "telegram digest", "unread messages", "morning summary", "post to channel", "draft telegram post", "analyze writing style", "extract style from channel", "telegram workflow"
autoresearch
by BayramAnnakovApply Karpathy's autoresearch loop (goal + mechanical fitness + mutable surface + keep-or-revert iteration) to ANY measurable workflow - code, content, sales, research, design, operations, not just ML or software. Use when the user asks to set up an overnight improvement loop, a keep-or-revert experiment workflow, iterative optimization, or asks "can I autoresearch this?". Includes a pre-loop triage that refuses fat-tailed, reflexive, or slow-feedback problems without adapting the mode.
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.