381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Fantasia1999
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Fantasia1999

quality-manager-qmr

by Fantasia1999
star 0

针对健康科技(HealthTech)和医疗科技(MedTech)公司的高级质量经理管理者代表 (QMR)。根据 ISO 13485 第 5.5.2 条提供质量体系治理、管理评审领导、合规性监督以及质量绩效监控。

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schedule Updated 3 months ago
Fantasia1999

board-deck-builder

by Fantasia1999
star 0

通过整合所有 C-suite 角色的视角,构建全面的董事会和投资者更新简报。适用于准备董事会会议、投资者更新、季度业务回顾或融资叙事。涵盖了结构、叙事框架、坏消息传达以及常见错误。

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schedule Updated 3 months ago
Fantasia1999

internal-narrative

by Fantasia1999
star 0

面向所有受众(员工、投资者、客户、候选人和合作伙伴)构建并维护一个连贯的公司故事。检测叙事矛盾,并确保针对每个受众的需求以相同的事实进行框架化。适用于准备投资者更新、全员大会演示、董事会沟通、招聘叙事、危机沟通,或当用户提到公司叙事、信息一致性、故事讲述、全员大会、投资者更新或危机沟通时。

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Fantasia1999

ux-researcher-designer

by Fantasia1999
star 0

适用于高级 UX 设计师/研究员的 UX 研究与设计工具包,包含数据驱动的用户角色生成、旅程地图绘制、可用性测试框架以及研究综合。用于用户研究、角色创建、旅程映射和设计验证。

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schedule Updated 3 months ago
Fantasia1999

qms-audit-expert

by Fantasia1999
star 0

针对医疗器械 QMS 的 ISO 13485 内部审计专业知识。涵盖审计规划、执行、不符合项分类和 CAPA 验证。适用于内部审计规划、审计执行、发现项分类、外部审计准备或审计方案管理。

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Fantasia1999

risk-management-specialist

by Fantasia1999
star 0

医疗器械风险管理专家,在整个产品生命周期中实施 ISO 14971。提供风险分析、风险评估、风险控制和生产后信息分析。当用户提到风险管理、ISO 14971、风险分析、FMEA、故障树分析、危险源识别、风险控制、风险矩阵、受益-风险分析、剩余风险、风险可接受性或上市后风险时使用。

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Fantasia1999

docx

by Fantasia1999
star 0

当用户想要创建、读取、编辑或操作 Word 文档(.docx 文件)时,使用此技能。触发条件包括:任何提及“Word 文档”、“word document”、“.docx”的情况,或要求生成包含目录、标题、页码或信头等格式的专业文档。当从 .docx 文件中提取或重组内容、在文档中插入或替换图片、在 Word 文件中执行查找和替换、处理修订或批注,或将内容转换为精美的 Word 文档时,也应使用此技能。如果用户要求将“报告”、“备忘录”、“信函”、“模板”或类似的交付成果作为 Word 或 .docx 文件,请使用此技能。请勿用于 PDF、电子表格、Google Docs 或与文档生成无关的常规编码任务。

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Fantasia1999

mdr-745-specialist

by Fantasia1999
star 0

医疗器械分类、技术文档、临床证据和上市后监督方面的 EU MDR 2017/745 合规专家。涵盖附录 VIII 分类规则、附录 II/III 技术文件、附录 XIV 临床评估以及 EUDAMED 集成。

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Fantasia1999

fda-consultant-specialist

by Fantasia1999
star 0

面向医疗器械公司的 FDA 合规顾问。提供 510(k)/PMA/De Novo 路径指导、QSR (21 CFR 820) 合规、HIPAA 评估以及设备网络安全建议。当用户提到 FDA 申报、510(k)、PMA、De Novo、QSR、上市前 (premarket)、对比器械 (predicate device)、实质等同性 (substantial equivalence)、HIPAA 医疗器械或 FDA 网络安全时使用。

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Fantasia1999

board-meeting

by Fantasia1999
star 0

用于战略决策的多智能体董事会会议协议。运行结构化的 6 阶段审议:上下文加载、独立的 C-suite 贡献(隔离,无交叉干扰)、批判性分析、综合汇总、创始人审核以及决策提取。当用户调用 /cs:board、发起董事会会议或对战略问题需要结构化的多视角高管审议时使用。

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Fantasia1999

ceo-advisor

by Fantasia1999
star 0

为战略决策、组织发展和利益相关者管理提供高管领导力指导。适用于规划战略、准备董事会演示、管理投资者、发展组织文化、做出高管决策、融资,或当用户提到 CEO、战略规划、董事会会议、投资者更新、组织领导力或高管战略时。

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Fantasia1999

chief-of-staff

by Fantasia1999
star 0

高管层编排层。将创始人的问题路由到正确的顾问角色,针对复杂决策触发多角色董事会会议,综合输出并跟踪决策。所有高管层交互均从此开始。自动加载公司上下文。

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Page 1 of 3

Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.