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.
Querying local SQLite index...
cargo-release
by ushironokoRust/Cargoプロジェクトのバージョン更新、GitHubリリース、crates.io publishを実行する。
octorus
by ushironokoGitHub PR review TUI with AI Rally (automated AI review cycles). Binary name is `or`.
release
by ushironokoabbrsのバージョン更新、GitHubリリース、crates.io publishを実行する。
create-pr
by ushironokoCreate a pull request from current changes. Use when the user requests PR creation, pushing changes for review, or submitting work for code review.
output-learn
by ushironokoClaude Codeセッションの技術的学びを抽出し、learnリポジトリにマークダウンとして保存・pushするスキル。
plan-review
by ushironokoPlan modeで作成したプランに対して、プロジェクト特性を分析し適切なレビューエージェントを自動選択・並列起動するスキル。引数なしで実行可能。
restoring-session
by ushironokoRestore session state (plan, target files, in-flight tasks) from bit issues created by the start-work skill. Use when resuming work after Claude Code restart, when the in-memory task list is empty but bit issues exist for the current branch, or when the user mentions "restore session", "repair session", "pick up where I left off", or references a prior bit-issue-tracked session.
smart-compact
by ushironokoセッションのコンテキストを分析し、重要な情報を保持するためのプロンプトを生成して /compact コマンドの実行を支援するスキル。コンテキストウィンドウが逼迫してきた時や、セッションを整理したい時に使用。
start-work
by ushironokoStart implementation work with worktree isolation and cross-session file conflict avoidance. Use this skill when beginning any non-trivial code change: after plan mode, when creating a new branch, implementing features, fixing bugs across multiple files, or refactoring. Also use when the user mentions worktree, bit issue, session coordination, or parallel work.
write-session
by ushironokoPersist mid-session progress (plan refinements, task progress notes, scope changes, decisions, blockers) to the bit issues created by start-work. Use as a checkpoint before context compaction, after meaningful progress (multiple tasks done, plan refined, scope shifted), before pausing work, or when the user mentions 'save session', 'snapshot', 'checkpoint', 'update bit issue', or 'persist progress'.
octorus
by ushironokoGitHub PR review TUI with AI Rally (automated AI review cycles). Binary name is `or`.
dig
by ushironoko現在のPlanに対して実装に必要な不足情報を繰り返し質問し、Planを充実させるスキル。/plan-reviewの前に使用する。
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.