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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bluesky
by maragudkGuide for posting content to the Bluesky social network using the bsky terminal app. This skill should be used proactively when working in public repositories and there is interesting, shareable content (new features, insights, achievements, or announcements worth sharing with the community). Use it when asked to post to Bluesky, or when content seems worth sharing publicly.
marimo
by maragudkGuide for creating and working with marimo notebooks, the reactive Python notebook that stores as pure .py files. This skill should be used when creating, editing, running, or deploying marimo notebooks.
brainstorm
by maragudkGuide for how to brainstorm an idea and turn it into a fully formed design.
collaboration
by maragudkGuide for collaborating on GitHub projects. This skill should be used when contributing to projects, creating PRs, reviewing code, or managing issues on GitHub.
diary
by maragudkWrite and maintain an implementation diary capturing what changed, why, what worked, what failed (with exact errors and commands), what was tricky, and how to review and validate. Activates proactively during non-trivial implementation work (new features, bug fixes, refactors, research spikes). Does not activate for trivial tasks like one-line fixes, config tweaks, or quick questions.
save-web-page
by maragudkGuide for saving a web page for offline use using the monolith CLI. Use this when instructed to save a web page.
garden
by maragudkAutonomous project gardening. Scans for maintenance issues (starting with documentation), picks one, fixes it in a worktree, self-reviews with competing agents, and opens a PR. Use when the user wants to tidy up the project, fix stale docs, or generally tend the codebase. Invoke with /garden.
atproto
by maragudkGuide for building on the AT Protocol (the "atmosphere") -- authoring Lexicons, building app views, consuming the firehose, working with identity (DIDs, handles), repositories, records, XRPC endpoints, and OAuth. Use this skill whenever the user is building anything on atproto/Bluesky/the atmosphere -- writing Lexicon JSON, calling com.atproto.* or app.bsky.* endpoints, parsing AT URIs (`at://...`), DIDs (`did:plc:...`, `did:web:...`), handles, TIDs, the indigo Go SDK (`github.com/bluesky-social/indigo`), the firehose / `subscribeRepos`, MSTs, CAR files, DAG-CBOR/DRISL, app views, feed generators, labelers, or PDS interactions. Triggers even if the user doesn't say "atproto" -- words like "lexicon", "PDS", "app view", "firehose", "did:plc", or `at://` URIs are enough.
bluesky
by maragudkGuide for posting content to the Bluesky social network using the bsky terminal app. This skill should be used proactively when working in public repositories and there is interesting, shareable content (new features, insights, achievements, or announcements worth sharing with the community). Use it when asked to post to Bluesky, or when content seems worth sharing publicly.
distill-book
by maragudkDistill a long book into a concise, structured set of learnings by processing it chapter by chapter with parallel subagents, then synthesizing the result -- optionally into a Claude Code skill. Works for any format the book comes in -- PDF, EPUB, Markdown, HTML, plain text. Use this skill whenever the user wants to summarize, distill, extract the core ideas from, or "turn into a skill" a book, ebook, manual, or other long-form document that is too large to read in one pass. Triggers include "distill this book", "summarize this book chapter by chapter", "extract the key principles from this PDF/ebook", "make a skill out of this book", or handing over a large multi-chapter document and asking what to learn from it.
unsloth
by maragudkGuide for fine-tuning LLMs, embedding models, vision-language models, and TTS models efficiently with Unsloth. Covers LoRA/QLoRA SFT, reinforcement learning (GRPO, DPO, ORPO, KTO), embedding fine-tuning with sentence-transformers, continued pretraining, and saving/exporting to GGUF, Ollama, or vLLM. Use this skill whenever the user mentions Unsloth, FastLanguageModel, FastSentenceTransformer, FastVisionModel, FastModel, or wants memory-efficient fine-tuning of open LLMs or embedding models on a single GPU, even if they don't explicitly say "Unsloth".
brainstorm
by maragudkGuide for how to brainstorm an idea and turn it into a fully formed design.
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.