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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logseq-lfm
by codekilnLook up Logseq-Flavored Markdown details beyond the always-on core: footnote syntax, nested fenced code blocks, bold-vs-backtick rendering pitfalls, the singular/plural naming rationale and aliases, the file-name-vs-link reference table, and the logical-vs-on-disk page distinction. Use when formatting footnotes, debugging why a ref/inline-code renders wrong, deciding a page name, or checking whether a page exists before creating a file. Baseline LFM rules live in logseq-core.
logseq-lfm
by codekilnLook up Logseq-Flavored Markdown details beyond the always-on core: footnote syntax, nested fenced code blocks, bold-vs-backtick rendering pitfalls, the singular/plural naming rationale and aliases, the file-name-vs-link reference table, and the logical-vs-on-disk page distinction. Use when formatting footnotes, debugging why a ref/inline-code renders wrong, deciding a page name, or checking whether a page exists before creating a file. Baseline LFM rules live in logseq-core.
logseq-pref
by codekilnLoad [[Logseq/Pref]] and linked pages for encode-wide garden preferences (page naming, namespaces, editorial defaults) before creating pages or entity types. Use when aligning new titles with [[Logseq/Pref/Page/Name]], refreshing prefs in-graph, or routing alongside logseq-entity for naming—not for per-entity definitions (those stay on [[Logseq/Entity/<Type>]]).
logseq-convert-md-to-lfm
by codekilnConvert standard Markdown (especially pasted from ChatGPT or similar) into Logseq-Flavored Markdown: nest code blocks inside bullets, fix heading-to-indent levels, drop horizontal rules, convert wide tables to label-value bullets, fix footnotes to URL-only, and replace generic link text. Use when the user pastes non-LFM markdown to import, asks to "convert to LFM", or to clean up an AI-generated dump. For table column alignment use logseq-table-formatter; for baseline LFM rules see logseq-core / logseq-lfm.
logseq-block-ids
by codekilnWork safely with Logseq block IDs (`id::` properties) and `((uuid))` block references when moving, copying, or removing blocks across graph pages. Use when relocating a block that has an id::, creating a new ((ref)) to a block, deciding whether an id:: can be removed, or diagnosing refs that won't render. Baseline safety (never backtick a live ref/link; keep id:: with its block) is in logseq-core; this skill has the full move/copy/reindex procedures.
logseq-asset-linker
by codekilnConstruct Logseq asset links: convert a macOS/absolute file path or a Logseq namespaced page name into a relative Markdown link (or file:/// link) into the graph's assets/ directory. Use when the user gives a file path or [[Namespace/Page]] and wants the asset/image/PDF link, an asset filename, or an asset folder path. Do not use for ordinary page wikilinks (logseq-core / logseq-link-hygiene).
logseq-youtube-notes
by codekilnFormat a YouTube video transcript inside a Logseq page into clean, navigable, timestamped Logseq-Flavored Markdown. Use when the user pastes a raw YouTube transcript, asks to organize/clean up video notes, add clickable {{youtube-timestamp}} headings, or structure a {{video ...}} block's notes. Do not use for non-video transcripts or for general LFM formatting (that is the logseq-lfm skill / logseq-core rule).
logseq-forum-post
by codekilnImport and structure a forum post (Cursor Forum, Reddit, Stack Overflow, or other forums) into a Logseq page with title link, metadata, Original Poster / Response / Related / My Notes sections, and site-specific user namespaces. Use when the user pastes a forum thread or asks to capture/import a forum discussion. For person hubs use the logseq-person command and logseq-entity skill. Do not use for blog posts/articles (logseq-import-blog) or non-forum content.
logseq-slides
by codekilnAuthor or fix Logseq slideshow pages where top-level bullets are section titles (H2) and nested bullets are individual slides with bullet points and a 16:9 image. Use when the user asks to build a slide deck/presentation in Logseq, add slides, or fix slide structure/image sizing. Do not use for ordinary page bullets or non-slide content.
logseq-link-hygiene
by codekilnCheck, resolve, and normalize Logseq wikilinks before or after editing graph pages. Use when adding links, importing notes, creating pages, reviewing unresolved references, preventing accidental stub pages, or replacing newly invented wikilinks with canonical existing pages or aliases. Applies to all Logseq graph work, not only entity pages.
logseq-flashcard
by codekilnCreate or maintain Logseq SRS flashcards and first-class Card entity pages in this garden. Use when the user asks to add a flashcard, factor out cards, create or audit Card pages, audit or fix Keyshort flashcards, ensure {{cards}} queries pick up cards, design or debug simple or advanced query expressions for card decks, or convert shortcut notes into review cards. New generic card work uses [[Card]] and [[Logseq/Entity/Card]]; legacy Keyshort audits may still encounter #card / [[card]]. Do not use for non-Logseq decks (e.g. repeater-only formats).
logseq-entity
by codekilnMetaskill for Logseq entity work in the active graph: open [[Logseq/Entity]] and [[Logseq/Entity/<Type>]] pages, dedupe, create or update instances per those type rules, infer candidates from journals, help initialize entity pages when asked, and record graph edits in today's journal (garddiff / Filed / Updated). All type-specific rules live in the graph; this skill does not list entity types or repo companion skills by name. Other skills in a repo may wrap narrow workflows—discover them from the graph, type pages, or the repo's skill layout. Use for: entity exists?, similar entities?, new entity page?, add entities from today's journal?, define or refresh entity types?
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.