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...
livelihood-support
by kazumasakawahara生活保護受給者尊厳支援データベース。7本柱モデルに基づき、二次被害防止と経済的安全を最優先とした支援情報を管理する。汎用neo4j MCPツールでCypherクエリを実行。
insight-agent
by kazumasakawahara現場スタッフが記録した感情ログやAI相談履歴を多角的に分析し、行動障害の悪化、体調不良、スタッフの負担増などの「予兆」を管理者に報告するスキル。「最近の変化」「様子がおかしい」「予兆」「リスク分析」「トレンド」「スタッフの状況」などの話題でこのスキルを使用すること。
livelihood-support
by kazumasakawahara⚠️【非運用 / DECOMMISSIONED 2026-05】この生活困窮者自立支援スキルは廃止され、運用していません。決して使用・発火しないでください。対応する Neo4j（port 7688）・MCP サーバー（livelihood-support-db）も撤去済みで、クエリは通りません。ユーザーが生活困窮支援を明示的に要求した場合は、本機能が非運用である旨を伝えてください。
sos-orchestrator
by kazumasakawaharaIntelligent agent that orchestrates emergency response based on context (Medical vs Behavioral).
ecomap-generator
by kazumasakawahara支援ネットワークをエコマップ（支援関係図）として可視化するスキル。HTML形式（Neo4j風）・Mermaid形式・SVG形式での出力に対応し、4種類のテンプレート（全体像、支援会議用、緊急時、引き継ぎ用）を提供。
parental-transition
by kazumasakawaharaManage the transition of care roles when a key person (parent) becomes unable to fulfill them.
care-improver
by kazumasakawaharaAnalyzes support logs to discover effective care strategies and suggests formalizing them.
emergency-protocol
by kazumasakawahara緊急時対応プロトコル。クライアントの安全に関わる情報を優先順位付きで取得し、二次被害を防止する。禁忌事項→推奨ケア→緊急連絡先→かかりつけ医→法的代理人の順で情報を提示。
support-hypothesis
by kazumasakawahara対象当事者（P-XXXX）の frontmatter relations を重みと意味付けに沿って辿り、知識層（60_Laws / 61_Guidelines / 63_Disorders / 64_Methods / 66_Services / 67_Orgs）を横断して「根拠付き支援仮説」を生成する。4レンズ（法令適合 / サービス適格 / 支援技法マッチング / 類似事例）で分析し、すべての主張に wikilink 出典を付す。計画相談専門員が支援計画作成・見直し時に使う想定。
emergency-protocol
by kazumasakawahara緊急時対応プロトコル。クライアントの安全に関わる情報を優先順位付きで取得し、二次被害を防止する。禁忌事項→推奨ケア→緊急連絡先→かかりつけ医→法的代理人の順で情報を提示。
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.