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...
project-manager
by takusaotomeProfessional project management skill aligned with PMBOK® 6th/7th Edition standards. Use this skill when you need to define requirements (ISO/IEC/IEEE 29148), review project plans, generate progress reports with Earned Value Management (EVM), conduct risk analysis, estimate costs, or provide project health assessments. Ideal for creating comprehensive project documentation, analyzing project performance metrics (SPI, CPI, EAC), managing risks across 14 categories, and ensuring stakeholder alignment. Triggers: "create project plan", "analyze project health", "calculate EVM", "risk assessment", "requirements definition", "progress report", "cost estimation", or requests involving PMBOK knowledge areas.
strategic-planner
by takusaotome戦略企画の専門スキル。中期経営計画、新規事業企画、事業ポートフォリオ分析を支援。 SWOT/PEST/Porter 5F分析、ビジネスモデルキャンバス、BCG/GE/Ansoffマトリクスなど MBA標準の戦略フレームワークを活用した体系的な戦略立案を行う。 Use when creating strategic plans, business proposals, portfolio analysis, or when applying strategic frameworks like SWOT, PEST, Porter's Five Forces, BCG Matrix. Triggers include「中期経営計画」「戦略企画」「事業ポートフォリオ」「SWOT分析」 「新規事業企画」「ビジネスモデル」「競争戦略」「成長戦略」
email-triage-responder
by takusaotomeAnalyze inbox emails to identify action-required items, prioritize by urgency/importance, classify by topic, and draft contextual replies. Use when triaging unread emails, prioritizing inbox, generating response drafts, or tracking email response status.
vendor-estimate-creator
by takusaotomeThis skill should be used when creating cost estimates and quotations for software development projects. Use this skill when you have an RFQ (Request for Quotation), project requirements, or a project description and need to create a comprehensive estimate with WBS, effort calculations, cost breakdowns, and ROI analysis. Supports Japanese (default) and English, with systematic work breakdown, effort estimation, and markdown-formatted estimate documents.
vendor-estimate-reviewer
by takusaotomeThis skill should be used when reviewing vendor estimates for software development projects. Use this skill when you need to evaluate whether a vendor's cost estimate, timeline, and approach are reasonable and whether the project is likely to succeed. This skill helps identify gaps, risks, overestimates, underestimates, and unfavorable contract terms. It generates comprehensive Markdown review reports with actionable recommendations to optimize costs while ensuring project success.
ma-cvp-break-even
by takusaotomeCVP（Cost-Volume-Profit）分析・損益分岐点分析スキル。固定費・変動費の構造分析、 限界利益率の算出、損益分岐点売上高/数量の計算、安全余裕率の評価、 目標利益達成に必要な売上高のシミュレーションを行う。多品目分析にも対応。 Use when: 損益分岐点を知りたいとき、新規事業や価格変更の採算シミュレーション、 固定費削減・変動費率改善の効果試算、What-if分析に使用。 Triggers: "損益分岐点", "CVP", "break-even", "限界利益", "contribution margin", "安全余裕率", "margin of safety", "固変分解", "変動費率"
ai-text-humanizer
by takusaotomeAI（LLM）が生成した日本語テキストの「AI臭」を検出・診断し、人間らしい文章にリライトするスキル。 Use when: 「AIっぽい文章を直して」「人間らしくリライトして」「AI臭を消して」「この文章をもっと自然にして」 「テキストのAI感を減らして」「機械っぽさを取りたい」 "make this sound more human", "remove AI tone", "humanize this text", "detect if this is AI-generated". 6つのAI特有パターン（視覚的マーカー、単調なリズム、マニュアル的構成、非コミット姿勢、抽象語の濫用、 定型メタファー）を正規表現ベースで検出し、0-100のAI臭スコアを算出。3つの人間化技法 （バランスを崩す・客観を崩す・論理を崩す）でリライトを実行する。 Note: 検出スクリプトは日本語テキスト専用。英語テキストの場合はClaude自身がreferences/を参照して分析・リライトする。
talent-acquisition-specialist
by takusaotome採用・人材戦略の専門スキル。職務記述書（JD）作成、採用計画策定、コンピテンシーモデル設計、 面接評価基準設計、オンボーディング計画をサポート。採用要件の明確化から内定後のフォローまで 一貫した人材獲得プロセスを支援。日英両言語のテンプレートを提供し、グローバル採用にも対応。 Use when: creating job descriptions, designing competency models, planning recruitment strategies, developing interview evaluation criteria, or creating onboarding plans. Triggers: "JD作成", "採用計画", "面接評価", "オンボーディング", "コンピテンシー", "人材獲得", "job description", "recruitment plan", "interview evaluation", "hiring", "talent acquisition"
operations-manual-creator
by takusaotome業務システムの操作手順書を構造化して作成するスキル。STEPフォーマット （Specific/Target/Expected/Proceed）による手順記述、ANSI Z535準拠の 注意・警告分類、トラブルシューティングガイドを含む包括的な操作マニュアルを 生成する。Use when creating operations manuals, standard operating procedures (SOP), user guides, or system operation guides. Triggers: "operations manual", "操作マニュアル", "手順書作成", "SOP", "操作手順", "standard operating procedure", "ユーザーガイド", "作業手順書", "運用マニュアル"
lean-six-sigma-consultant
by takusaotomeComprehensive Lean Six Sigma consulting skill supporting all belt levels (White Belt to Master Black Belt). Use this skill for DMAIC/DMADV project execution, Lean waste elimination (VSM, 8 Wastes/DOWNTIME, 5S), statistical analysis (process capability Cp/Cpk, control charts, hypothesis testing), and Six Sigma training/education. Triggers include "improve process", "reduce defects", "sigma level", "DMAIC project", "value stream mapping", "Kaizen", "process capability", "control chart", "root cause analysis", "5 Whys", "Fishbone diagram", "FMEA", "DOE", or requests involving process improvement methodologies.
ma-budget-actual-variance
by takusaotome予算実績差異分析スキル。勘定科目タイプ（収益/費用）に応じた有利・不利差異の自動判定、 差異の分解（価格差異・数量差異）、重要度ランキング、根本原因の仮説提示を行う。 CSVデータのアップロードによる自動分析に対応。 Use when: 予算と実績の比較分析を行いたいとき。月次・四半期の予実管理レポート作成、 差異の原因分析、経営会議向けの予実サマリ作成に使用。 Triggers: "予実差異", "予算実績", "budget variance", "budget vs actual", "予算対比", "差異分析", "variance analysis"
purchase-request-generator
by takusaotomeGenerate formal IT/hardware purchase request documents from informal requirements. Use when creating purchase justifications, cost-benefit analyses, ROI calculations, vendor comparisons, or MARP presentation slides for management approval.
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.