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.
Querying local SQLite index...
deep-research
by KjdraganConduct autonomous, deep research tasks using the Gemini Deep Research Agent (preview). Use ONLY when the user explicitly requests "deep research" or "google deep research" to perform extensive literature review, analyze a topic deeply, or generate a comprehensive research report requiring multi-step web search, reading, and synthesis. Do NOT trigger for standard web searches or generic questions unless "deep research" is mentioned.
zread-dependency-docs
by KjdraganRead documentation and code from open source GitHub repositories using the ZRead MCP server
arxiv-specialist
by KjdraganEnable interaction with arXiv through the arxiv-mcp-server. Trigger this skill when the user explicitly requests to find, search for, parse, summarize, or analyze academic research papers from arXiv. Make sure to use this skill whenever academic computer science, machine learning, physics, or quantitative biology papers are requested.
agentmail
by KjdraganUse the official AgentMail MCP tools for Simone's own inbox. Send outbound mail with `mcp__agentmail__send_message`, reply with `mcp__agentmail__reply_to_message`, and prepare local attachments with `prepare_agentmail_attachment`. Do not use bash, curl, SDK scripts, or CLI commands for in-session email delivery.
link-purchase
by KjdraganUse this skill when the user gives an explicit, parseable purchase intent — e.g. "buy X from Y for $Z", "pay $N to merchant.com", "complete the checkout at <url>". Generates a Link spend request, surfaces the Link approval URL for the user to tap-approve in the Link app, and handles the post-approval flow. NEVER triggers from offhand mentions like "I should buy that someday". The user must specify a merchant, amount, and what's being bought.
agent-interview
by KjdraganA skill that allows the agent to conduct a structured or unstructured interview with the user to gather information.
task-forge
by KjdraganBuild a structured task-skill to accomplish any goal, then execute and iterate until done. USE THIS SKILL when the user says "task forge this", "forge a skill for X", "build me a skill to do X", "figure out how to do X", "don't plan, just try it", "let the agent handle it", or any time they want to tackle a non-trivial task by letting the agent explore and discover the approach rather than pre-engineering a detailed plan. Also use when converting a vague idea into an actionable, executable skill package, or when the user wants to create a skill for a one-off task. Triggers on: "task forge", "forge", "build a skill", "create a skill for", "skill for this task", "package this as a skill", "make a skill", "one-off skill".
oversized-file-chunked-reading
by KjdraganRecover when a Read is rejected for being too large, by locating the relevant region with grep/rg first and then issuing a targeted Read with offset and limit instead of retrying the full read. Use whenever a Read fails with "File content ... exceeds maximum allowed tokens (25000). Use offset and limit parameters to read specific portions of the file, or search for specific content instead of reading the entire file", or with "File content (262.5KB) exceeds maximum allowed size (256KB). Use offset and limit parameters...", or any "exceeds maximum allowed size", ">256KB", "file too large to read", "read rejected", "25000 token limit", or "256KB limit" message. Use when you need one function or region out of a huge source file, or need to scan a big log/JSONL/bundle where only matching lines matter, or wonder "how do I read a giant file" or "how do I chunk through a big file". Also use when you want to read a file in chunks, chunk the file, do a chunked read, read the file in pieces, page through a big file, or
project-scaffolder
by KjdraganScaffold a fully-provisioned VPS project with FastAPI backend, Vite+React frontend, PostgreSQL, Claude Agent SDK, Infisical secrets, GitHub CI/CD, MkDocs documentation, and curated agent skills. Creates everything needed to start building immediately. Use this skill whenever the user asks to scaffold, create, spin up, bootstrap, or start a new project — even if they just say "new project" or "make me an app called X". Also use when they mention wanting a new codebase, a new service, a new web application, or anything that implies standing up a fresh FastAPI/React stack on the VPS. Do NOT improvise project setup manually (e.g. uv init, mkdir) — always use this skill's scaffold.py script for consistency and completeness.
project-scaffolder
by KjdraganScaffold a fully-provisioned VPS project with FastAPI backend, Vite+React frontend, PostgreSQL, Claude Agent SDK, Infisical secrets, GitHub CI/CD, MkDocs documentation, and curated agent skills. Creates everything needed to start building immediately. Use this skill whenever the user asks to scaffold, create, spin up, bootstrap, or start a new project — even if they just say "new project" or "make me an app called X". Also use when they mention wanting a new codebase, a new service, a new web application, or anything that implies standing up a fresh FastAPI/React stack on the VPS. Do NOT improvise project setup manually (e.g. uv init, mkdir) — always use this skill's scaffold.py script for consistency and completeness.
notebooklm-orchestration
by KjdraganOrchestrate NotebookLM operations for UA with a hybrid MCP-first and CLI-fallback execution model, backed by Infisical-injected auth seed and VPS-safe guardrails. Use whenever the user mentions NotebookLM/notebooklm/nlm, notebooks, NotebookLM source ingestion, NotebookLM research, podcast/audio overview generation, report/quiz creation, flashcards, slide decks, infographics, downloads, sharing, or NotebookLM automation workflows. Route execution to `notebooklm-operator` by default.
gemini-tts-narrator-tf
by KjdraganNarrate text into a high-quality MP3 audiobook using Google Cloud Text-to-Speech API with Gemini TTS models. Accepts local file paths (desktop or VPS), URLs, or raw text. Produces native MP3 with warm narration style, smart paragraph chunking with pause markup, and multi-model support. Trigger on "narrate", "read aloud", "audiobook", "TTS this story", "convert to audio", "read this to me", or "narrator".
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.