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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my-skill-creator
by YnakatsukaInteractive guide for creating and improving Claude Code skills (SKILL.md files). Use when the user explicitly asks to create, update, or fix a **skill definition**. Triggers: "スキルを作って", "スキル作成", "create a skill", "new skill", "build a skill", "improve this skill", "update skill", "fix my skill", "skill doesn't trigger". Do NOT use for general coding tasks, PR creation, code review, or any task that merely contains the word "create" without referring to skill authoring.
my-update-models
by YnakatsukaCheck the latest Claude (Anthropic), OpenAI Codex, and Google Gemini model releases from official primary sources, then update model selections in this dotfiles repo (dot_claude/settings.json, dot_codex/config.toml.tmpl, dot_gemini/settings.json). Also scans the invoking repository for hardcoded model IDs in GitHub Actions workflows, Python code, and shell scripts, and offers to bump them. Use when the user asks to "モデル更新", "モデルを最新に", "最新モデル確認", "model bump", "update models", or "claude/codex/gemini のモデル更新". Do NOT use for one-off model selection in a single conversation, general model questions, or non-config code changes.
my-agent
by YnakatsukaDelegate tasks to an external CLI agent (OpenAI Codex or Google Gemini) for second opinions or parallel execution. Use when the user explicitly mentions "codex" or "gemini" or asks to delegate work to one of them (e.g., "codexで実行", "codexに聞いて", "geminiで実行", "Geminiに聞いて", "run with codex", "ask gemini"). Do NOT use for general coding tasks that don't mention codex or gemini.
my-worktree
by YnakatsukaCreate a git worktree using the project's `{repo}-worktree/{branch}` convention, always pulling origin/staging, origin/main, and origin/master first so the new branch is based on up-to-date refs. Use when the user asks to create a new worktree or start work on a new branch in a worktree (e.g. "ワークツリー作って", "worktree 作成", "新しい作業ブランチ", "新規ブランチで作業"). Do NOT use for removing/listing worktrees (use `gwc` / `git worktree list`), for the PR flow (use my-pr — it creates its own worktree on protected branches), or for SDD-driven feature work (use my-sdd — Phase 3-0 has its own worktree gate).
my-ccv
by YnakatsukaManage Claude Code Viewer (CCV): register HTML artifacts or schedule jobs via CCV local API. Use when user asks to create dashboards, register artifacts to CCV, manage CCV artifacts, schedule CCV jobs, or manage CCV scheduler (e.g., "CCVに登録", "アーティファクト作成", "CCV artifact", "定性評価を登録", "スケジュール登録", "定期実行", "CCV schedule"). Do NOT use for general HTML creation, static site generation, or Anthropic remote triggers.
my-diagnose
by YnakatsukaDisciplined bug and performance-regression diagnosis loop: build a feedback loop, reproduce, hypothesize, instrument, fix, and regression-test. Use when the user reports a bug, says something is broken/failing/throwing, asks to debug or diagnose, or describes a performance regression. Do NOT use for planned feature work or broad architecture review.
my-disk-cleanup
by YnakatsukaClean up disk space by removing caches and unnecessary files on macOS and Ubuntu. Targets: Docker (images, build cache, volumes), package managers (Homebrew, apt, pip, npm, yarn), and temporary files (__pycache__, .mypy_cache, Xcode DerivedData). Reports disk usage before/after with freed space summary. Use when the user asks to free disk space, clean caches, or mentions "ディスク整理", "キャッシュ削除", "disk cleanup", "free space", "clean docker", "clean caches". Do NOT use for file organization, deduplication, or finding large files.
my-epic
by YnakatsukaOrchestrate medium-to-large software development by turning a broad goal into an implementation and verification plan made of PR-sized subtasks and operational nodes, defining verification harnesses and gates for each node, then driving implementation, operation execution, and PR creation. Use when the user asks for large-scale development planning, PR decomposition, multi-PR execution, technical selection, or autonomous delivery of a broad epic. Do NOT use for a single small bug fix, one obvious PR, pure code review, or ordinary SDD work that already fits in one spec/PR.
my-guided-tour
by YnakatsukaExplain code, architecture, product behavior, or a technical concept from a higher-level perspective, then create an HTML visual explanation and optional quiz matched to the user's understanding goal and role. Use when the user asks to zoom out, wants broader context, is unfamiliar with an area, wants a diagram, asks how something fits into the bigger picture, or wants enough context to implement safely. Include implementation locations and code/entity anchors when depth requires it. Do NOT use for direct implementation, refactoring, bug fixing, or scaffolding exercises.
my-pr
by YnakatsukaUnified pull request workflow: prepare a safe branch, run the integrated simplify workflow and parallel reviews, apply fixes, create/update a GitHub draft PR, and verify CI plus automated review comments before marking ready. Subcommands: `create` (simplify + PR, skip code review), `review` (parallel review only), `simplify` (simplify only), `verify` (existing PR checks/reviews only). Use when creating PRs, self-reviewing changes, simplifying PR changes, or requesting "PR作成", "レビュー", "簡素化". Do NOT use for responding to others' review comments or reviewing external repositories.
my-refactor
by YnakatsukaScan a codebase for refactoring opportunities, write a SARIF-compatible report, and register each finding as a GitHub issue labelled by severity, effort, and Fowler category. After issue creation, users are directed to `/my-sdd <feature-name>` to plan and implement each refactor (one issue = one spec = one PR). Use when the user asks to "リファクタリング候補洗い出し", "技術的負債リスト化", "refactor scan", "refactoring issues", or requests surveying a repo for refactor work. Do NOT use for applying refactors (delegate to `/my-sdd`), code review of a specific PR (use `my-pr review`), or for fixing a specific bug.
my-release-note
by YnakatsukaGenerate release notes from merged PR history and commits. Categorizes changes by type and creates GitHub releases with semantic versioning. Use when the user asks to create a release or generate release notes (e.g., "リリースノート", "release note", "リリース作成"). Do NOT use for changelogs, commit summaries, or PR descriptions.
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.