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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spectra-ask
by PsychQuantQuery openspec/documents and answer questions
gemini-cli-guide
by PsychQuantQuery Google Gemini CLI configuration, features, and documentation. Use this skill proactively when the conversation involves: - Gemini CLI installation, setup, or authentication - Gemini CLI configuration (GEMINI.md, settings, themes) - Gemini CLI commands, flags, slash commands - Gemini CLI features (sandbox, plan mode, yolo, skills, hooks) - Gemini CLI model routing, model steering - Gemini CLI extensions, MCP, ACP mode - Gemini CLI headless/non-interactive mode - Gemini CLI policy engine, trusted folders - Gemini CLI session management, checkpointing, rewind
grind
by PsychQuantLean 4 自動證明磨削。廣度優先掃描所有 sorry，按難度排序， 逐一嘗試證明，每解一個就 commit。搭配 lean-prover agent 做深度嘗試。 當用戶說「開始證明」、「grind」、「消除 sorry」、「prove all」時觸發。
mcp-sign-pipeline
by PsychQuantApply Developer ID signing + notarization pipeline to a Swift MCP / CLI project. Required for macOS 26 — ad-hoc signed binaries can no longer trigger TCC permission dialogs. Use when creating new MCP/CLI (mcp-new-app / cli-new-app integration) OR upgrading an existing project that needs to ship signed releases. Templates are extracted from che-ical-mcp PR
tidyverse-guide
by PsychQuantQuery tidyverse package documentation (dplyr, ggplot2, tidyr, purrr, readr, stringr, forcats, tibble, lubridate). Use this skill proactively when the conversation involves: - tidyverse packages or pipe workflows (%>%, |>) - dplyr verbs (filter, mutate, summarise, group_by, join) - ggplot2 plotting (geom_, aes, theme, scale_) - tidyr reshaping (pivot_longer, pivot_wider, nest, unnest) - purrr functional programming (map, walk, reduce) - readr/readxl data import - stringr string manipulation - lubridate date/time handling
apa-rewriter
by PsychQuantRewrite text to APA 7th edition style. Use when user asks to "rewrite to APA", "make APA compliant", "check APA style", or needs help with psychology/social science academic writing.
axiom-lookup
by PsychQuant搜尋公理化系統中的公理、定理和概念。支援跨領域全文搜尋和特定領域查詢。
proofread
by PsychQuantJSONL-driven 6-layer proofread workflow for math manuscript. Each prop in main.jsonl becomes a [ ] checklist item in manuscript/.proofread/<file>.md; walk through L1-L5 + location-drift per prop; mark [x] (CLEAN) / [~] (finding) / [-] (out of scope). L1 = text-claim match (asserts truly atomic + faithful paraphrase) L2 = claim_type fit (axiom non-derivable / definition has equality / commentary not derived / etc.) L3 = cite completeness (all external refs declared) L4 = cite validity (each cited prop logically implies this prop's asserts) L5 = evidence_class fit (derived needs cites / axiomatic truly axiom / etc.) +location drift (claimed line range matches main.tex actual) Use when: pre-submission final polish, post large rewrite, Hsu-approval-pending area, validating prop-extraction quality. NOT for daily micro-edit (use sync rule + validator). v0.1.0 SCAFFOLDING — execution body TODO. Methodology frozen in PsychQuantHsu/psychophysical_representations/manuscript/.proofread/main_jsonl_l4_walk.md.
shiny-guide
by PsychQuantQuery Shiny framework documentation for building interactive web applications in R. Use this skill proactively when the conversation involves: - Shiny app development (ui, server, reactive) - Shiny UI components (fluidPage, navbarPage, tabsetPanel) - Reactive programming (reactive, observe, eventReactive, reactiveVal) - Shiny modules (moduleServer, NS) - shinydashboard, bslib, shinyWidgets - Deploying Shiny apps (shinyapps.io, Posit Connect) - R Shiny debugging, testing (shinytest2)
r-docs-guide
by PsychQuantQuery R package documentation across all ecosystems at once (CRAN, tidyverse, Bioconductor). Use this skill when: - Looking up R package usage, functions, or vignettes - Comparing packages for the same task (e.g., data.table vs dplyr) - User says "R docs", "how to use this R package", or asks about R functions - Need to find the right R package for a task
apple-oauth-setup
by PsychQuantSet up Apple Sign In for Web on Apple Developer Portal via Safari + AppleScript. Use when user says "Apple Sign In", "Apple OAuth", "設定 Apple 登入", "建立 Services ID", "generate .p8 key", "Apple client secret", "developer.apple.com 設定", or needs to configure Apple OAuth for Supabase/web apps. Also handles JWT client secret generation from .p8 keys.
axiom-validate
by PsychQuant驗證公理化系統的結構完整性（ASBE 合規）和跨領域一致性（無矛盾）。
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.