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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db-schema-change
by hiroppyUse when adding/modifying database tables or columns in Drizzle ORM schema
ui-component
by hiroppyUse when creating new UI components under apps/web/src/components/
crawler-scraper
by hiroppyUse when adding new scraping targets to the crawler
drizzle-erd
by hiroppyUse when needing to visualize database schema, generate ERD diagrams from Drizzle ORM schemas, or understand table relationships
sync-upstream-features
by hiroppyInvestigate whether the latest hook events and features from Claude Code / Codex CLI are covered by tmux-agent-sidebar's current implementation, and report gaps. Use on requests like "check diff with upstream", "look for new hooks", "check changelog for unsupported features". Does not implement anything.
regenerate-captures
by hiroppyRegenerate the Starlight website's capture PNGs (hero / agent-pane-focus / activity-focus / git-focus / worktree-spawn / og-image) from the tmux scenario fixtures. Trigger on "regenerate captures", "rebuild capture images", "update hero image", "update agent pane image", "再生成 画像", "キャプチャ更新", and any request that changes `fixtures/scenarios/**` or the seeding logic in `_lib.sh`.
version-release
by hiroppyUse when bumping version, creating a release tag, or pushing a version tag. Triggers on "version up", "release", "tag push", "bump version".
ui-showcase
by hiroppyDisplay all agent pane UI elements simultaneously in the sidebar for visual verification. Trigger on "UI check", "showcase", "UI demo", "show all patterns", "verify panel display", "UIを確認したい", "showcaseして". Use after UI changes to visually confirm rendering.
docs-audit
by hiroppyAudit docs/ design documents, specs, and implementation plans against current source code for drift. Outputs a drift report and updates documents. Triggers on "check if docs are up to date", "diff docs vs code", "update design docs", "docs consistency check", "verify specs match source", "check plan completion status". Most effective after code or documentation changes.
init-article
by hiroppyInitialize a new blog article with interactive prompts for title, description, tags, and image. Automatically generates slug from title and validates uniqueness.
deps-merger
by hiroppyMerge all open dependency-update PRs (Dependabot / Renovate) in the current repository. Auto-fixes failing CI (lockfile regen, lint/format/type/test errors), squash-merges minor/patch updates automatically, and pauses for user approval on major updates after CI is green. Use this skill whenever the user asks to handle dependency PRs, deps PRs, dependabot PRs, renovate PRs, "deps全部マージ", "依存更新マージ", "deps PR対応", "depsまとめてマージ", or wants to bulk-process bot-authored upgrade PRs.
pr-monitor
by hiroppyMonitor a GitHub PR for CI failures and review comments, then automatically fix issues. Use this skill when the user wants to watch a PR, fix CI, respond to review comments, or automate PR maintenance. Triggers on "PR監視", "PRを見て", "CIが落ちた", "レビュー対応", "pr monitor", "watch pr", "fix ci", or "pr fix".
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.