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Chuinb

skill-yizhiyanhua-ai-chuinb-skill-chuinb-skill · by yizhiyanhua-ai

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$ agentstack add skill-yizhiyanhua-ai-chuinb-skill-chuinb-skill

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

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No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

Industry Mastery: 行业速成大师

> Transform from outsider to insider in hours, not months.

This skill creates immersive, research-backed learning experiences that make users feel like industry veterans. It combines proven learning methodologies with real-time web research to deliver knowledge that sticks.

Core Philosophy

The Three Pillars

┌─────────────────────────────────────────────────────────────────┐
│                    INDUSTRY MASTERY                             │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   🧠 FEYNMAN TECHNIQUE        ⚛️ FIRST PRINCIPLES               │
│   "If you can't explain       "Boil everything down             │
│    it simply, you don't        to fundamental truths,           │
│    understand it well enough"   then reason up from there"      │
│                                                                 │
│                    📊 80/20 PARETO                              │
│                    "20% of knowledge delivers                   │
│                     80% of practical value"                     │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

⚠️ CRITICAL: Execution Flow (MUST FOLLOW)

Overview

┌─────────────────────────────────────────────────────────────────┐
│                    EXECUTION FLOW                               │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Phase 1: User Profiling ──────────────────────────────────     │
│           Ask 3 questions about goal, background, time          │
│                          ↓                                      │
│  Phase 2: Deep Research ───────────────────────────────────     │
│           WebSearch + WebFetch for content                      │
│                          ↓                                      │
│  Phase 3: Media Acquisition (MANDATORY) ───────────────────     │
│           Download images + videos + generate AI images         │
│                          ↓                                      │
│  Phase 4: Ask Save Path ───────────────────────────────────     │
│           Use AskUserQuestion to get save location              │
│                          ↓                                      │
│  Phase 5: Generate & Save ─────────────────────────────────     │
│           Create markdown file with embedded media              │
│                          ↓                                      │
│  Phase 6: Interactive Follow-up ───────────────────────────     │
│           Offer deep dives, practice scenarios                  │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Phase 1: User Profiling (MANDATORY FIRST STEP)

Before ANY research begins, gather user context through conversational questions:

┌─────────────────────────────────────────────────────────────────┐
│  🎯 USER PROFILING QUESTIONS                                    │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. 「你的目标」What do you want to achieve?                    │
│     □ 社交谈资 (Casual conversation)                            │
│     □ 职业转型 (Career transition)                              │
│     □ 投资决策 (Investment decisions)                           │
│     □ 合作洽谈 (Business collaboration)                         │
│     □ 纯粹好奇 (Pure curiosity)                                 │
│                                                                 │
│  2. 「当前背景」What's your current profession/background?      │
│     (This helps tailor analogies and explanations)              │
│                                                                 │
│  3. 「时间预算」How much time can you invest?                   │
│     □ 30分钟速览 (Quick overview)                               │
│     □ 2小时深入 (Deep dive)                                     │
│     □ 持续学习 (Ongoing learning)                               │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Present these questions conversationally, not as a form. Adapt based on context clues in the user's initial request.


Phase 2: Deep Research

Execute comprehensive web research covering:

  1. Industry Fundamentals
  • Core business models and value chains
  • Key players (companies, organizations)
  • Market size and growth trends
  1. Key Figures & Events
  • Influential people (founders, thought leaders, critics)
  • Historical milestones and turning points
  • Recent news and developments
  1. Professional Vocabulary
  • Industry jargon and acronyms
  • Insider phrases and expressions
  • Common misconceptions to avoid
  1. Real Cases & Stories
  • Success stories with specific details
  • Notable failures and lessons learned
  • Current controversies or debates

Research Tools:

  • Use WebSearch for current information, news, trends
  • Use WebFetch for detailed article content

2.1 Source Credibility Guardrails (MANDATORY)

在整理研究结果前,必须执行以下可信度约束:

  • 最少来源数量:至少引用 5 个有效来源
  • 来源类型配比(至少覆盖 2 类):
  • 官方/监管/机构报告(公司财报、政府、协会、咨询机构)
  • 主流媒体或行业媒体
  • 专业研究文章或高质量数据库
  • 时间窗口要求
  • 至少 2 条信息来自最近 12 个月
  • 涉及“最新进展”时,优先最近 90 天来源
  • 可追溯性:关键结论后面必须附来源链接或出处说明

2.2 Conflict Handling Protocol

当不同来源出现冲突信息时,遵循以下流程:

  1. 明确列出冲突点(例如市场规模、增长率、关键事件日期)
  2. 给出每个版本的来源与时间
  3. 解释可能的差异原因(统计口径、地区范围、时间维度)
  4. 给出“当前最可信结论 + 不确定性说明”

2.3 Citation & Evidence Rules (MANDATORY)

  • 严禁编造来源:找不到可信出处时,明确写“暂无权威来源支持”
  • 关键结论必须可追溯:核心观点后附 [来源名, 日期] + 链接
  • 数据必须带时间戳:市场规模、增速、排名等信息需注明统计年份

推荐在输出中使用如下格式:

### 关键结论与证据

1. 结论 A(置信度:高)
   - 证据 1: [来源名称, 2026-03-10](https://example.com)
   - 证据 2: [来源名称, 2025-11-22](https://example.com)
   - 备注: 不同机构口径略有差异,已按“全球口径”统一

2.4 Reference Module Routing

按主题选择最小必要参考文件,避免过载:

  • 术语与表达:references/jargon-patterns.md
  • 行业分类研究框架:references/industry-templates.md
  • 学习方法解释:references/learning-theories.md

仅加载与当前用户目标相关的部分,不必每次全量展开。


Phase 3: Media Acquisition (MANDATORY - DO NOT SKIP)

⚠️ THIS PHASE IS REQUIRED FOR EVERY EXECUTION

媒体素材是让学习笔记生动有力的关键。必须执行媒体获取流程,但数量和类型根据内容需要灵活调整。

3.1 媒体获取原则

核心原则:根据内容类型选择最合适的获取方式

┌─────────────────────────────────────────────────────────────────┐
│                    媒体获取决策树                                │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  需要什么类型的图片?                                            │
│       │                                                         │
│       ├─→ 事实性图片 ──→ 优先网络下载真实图片                    │
│       │   • 人物照片(创始人、专家、名人)                       │
│       │   • 产品图片、公司 Logo                                  │
│       │   • 剧照、海报、新闻配图                                 │
│       │   • 历史事件照片                                         │
│       │   • 实物图片(咖啡豆、芯片、汽车等)                     │
│       │                                                         │
│       └─→ 概念性图片 ──→ 使用 AI 生成                           │
│           • 价值链/生态系统图                                    │
│           • 流程图、关系图                                       │
│           • 抽象概念的视觉化                                     │
│           • 数据可视化、信息图                                   │
│           • 氛围图、风格示意图                                   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

3.2 媒体数量指南(灵活调整)

不设硬性数量限制,根据内容丰富度和主题特点决定:

| 内容类型 | 建议媒体配置 | 说明 | |----------|-------------|------| | 人物密集型(如影评、商业领袖) | 3-5 张人物照片 + 1-2 个访谈视频 | 优先下载真实照片 | | 概念密集型(如金融、技术) | 2-4 张概念图 + 1-2 个解释视频 | 优先 AI 生成 | | 案例密集型(如商业分析) | 2-3 张案例图 + 1-2 张流程图 | 混合使用 | | 视觉艺术类(如电影、设计) | 4-6 张剧照/作品图 + 1-2 个片段 | 优先下载真实图片 |

3.3 图片获取策略

策略 A:事实性图片 → 优先网络下载

适用场景

  • 人物照片(创始人、CEO、专家、艺术家、导演、演员等)
  • 产品图片、公司 Logo、品牌视觉
  • 电影剧照、海报、专辑封面
  • 新闻事件配图、历史照片
  • 实物图片(食物、设备、建筑等)

获取方式

# 方式 1: 使用 media-downloader(需要 API Key)
python ~/.claude/skills/media-downloader/media_cli.py image "关键词" -n 数量 -o 输出目录

# 方式 2: 通过 WebSearch 找到图片 URL,然后下载
# 搜索关键词示例:
# "[人名] portrait photo"
# "[公司名] logo high resolution"
# "[电影名] movie poster"
# "[产品名] product image"

搜索关键词模板

人物照片:   "[姓名] portrait" / "[姓名] headshot" / "[姓名] photo"
公司Logo:   "[公司名] logo png" / "[公司名] brand"
电影海报:   "[电影名] movie poster" / "[电影名] official poster"
剧照:       "[电影名] still" / "[电影名] scene" / "[电影名] screenshot"
产品图:     "[产品名] product photo" / "[产品名] official image"
策略 B:概念性图片 → 使用 AI 生成

适用场景

  • 行业价值链、生态系统图
  • 业务流程图、工作流程
  • 抽象概念的视觉化表达
  • 关系图、层级图
  • 氛围图、风格示意图
  • 找不到合适真实图片时的备选

使用 zimage-skill 生成

MODELSCOPE_API_KEY="your-key" python3 ~/.claude/skills/zimage-skill/generate.py "prompt" "output.jpg"

Prompt 模板

# 流程图/价值链
"[主题] value chain diagram, minimalist infographic style, [color] color scheme,
professional business design, clean flat design, white background"

# 概念图
"[概念] concept visualization, modern illustration style, simple and clear,
professional corporate design"

# 氛围图
"[主题] aesthetic, cinematic atmosphere, [风格描述], professional photography style"

# 生态系统图
"[行业] ecosystem diagram, showing key players and relationships,
minimalist business infographic, clean design"
策略 C:备选方案

当以上方式都失败时:

  • 提供外部链接:[查看图片](url)
  • 使用文字描述代替
  • 使用 Mermaid 图表(适用于流程图)

3.4 视频获取策略

使用 media-downloader 下载 YouTube 视频

python ~/.claude/skills/media-downloader/media_cli.py youtube "URL" -o "输出目录" --end 120

视频类型与搜索关键词

| 视频类型 | 搜索关键词模板 | 适用场景 | |----------|---------------|----------| | 解释性视频 | "[概念] explained", "how [X] works" | 复杂概念讲解 | | 入门视频 | "[行业] 101", "[行业] for beginners" | 行业入门 | | 人物访谈 | "[人名] interview", "[人名] talk" | 人物介绍 | | TED 演讲 | "[主题] TED talk" | 思想启发 | | 纪录片片段 | "[主题] documentary" | 深度内容 | | 新闻报道 | "[事件] news report" | 时事案例 |

视频要求

  • 时长:根据内容价值灵活裁剪(建议 60-180 秒)
  • 内容:与主题直接相关
  • 质量:至少 720p

3.5 媒体文件命名规范

人物照片:   person-[姓名拼音或英文].jpg
概念图:     diagram-[描述].jpg
剧照/海报:  poster-[作品名].jpg / still-[作品名].jpg
案例图:     case-[案例名].jpg
产品图:     product-[产品名].jpg
视频:       video-[主题].mp4

3.6 媒体嵌入格式 (Obsidian)

图片: ![[media/filename.jpg]]
视频: ![[media/filename.mp4]]
带说明: ![[media/filename.jpg|这是图片说明]]

3.7 媒体获取检查清单

在完成媒体获取后,确认:

  • [ ] 所有提到的关键人物都有对应图片(真实照片优先)
  • [ ] 核心概念有视觉化表达(AI 生成或真实图片)
  • [ ] 至少有 1 个相关视频片段
  • [ ] 图片和视频数量与内容丰富度匹配
  • [ ] 所有媒体文件已下载到本地 media 文件夹
  • [ ] 文件命名清晰规范

3.8 媒体失败降级策略(MANDATORY)

当媒体获取失败时,不允许直接跳过,必须按顺序降级:

  1. 事实图失败:尝试 AI 生成替代图,并明确标注“AI 生成示意图”
  2. AI 图失败:改用 Mermaid/ASCII 图或文字结构化描述
  3. 视频失败:提供高质量外链 + 60-120 秒关键内容摘要

3.9 版权与合规提示(MANDATORY)

在文档的“延伸阅读”或末尾补充:

  • 媒体来源说明(来源平台/作者/链接)
  • 版权状态提示(仅学习参考或可商用)
  • 商业使用风险提醒(建议优先使用可复用许可素材)

Phase 4: Ask Save Path (MANDATORY)

⚠️ 在生成内容之前,必须询问用户保存路径

使用 AskUserQuestion 工具询问用户:

问题: "请告诉我你想把学习笔记保存到哪里?"

选项:
1. 当前目录 (Current directory)
2. 桌面 (Desktop)
3. 自定义路径 (Custom path)

如果用户选择自定义路径:

  • 等待用户输入完整路径
  • 验证路径是否存在,不存在则创建

默认文件结构:

[用户指定路径]/
├── [主题]速成指南.md          # 主文档
└── media/                      # 媒体文件夹
    ├── diagram-*.jpg
    ├── person-*.jpg
    ├── case-*.jpg
    └── video-*.mp4

Phase 5: Content Generation & Save

5.1 Output Structure Template

# [Industry/Field Name] 行业速成指南

> 🎯 **你的学习目标**: [Personalized based on user profile]
> ⏱️ **预计阅读时间**: X 分钟
> 📅 **生成日期**: YYYY-MM-DD

---

## 一句话看懂这个行业

[Feynman-style explanation in ONE sentence that a 12-year-old could understand]

---

## 第一性原理:行业的本质

[Break down to fundamental truths. What problem does this industry solve? Why does it exist?]

![[media/diagram-value-chain.jpg]]
*行业价值链图解*

### 核心价值链
[Visual diagram or clear explanation of how value flows]

### 关键驱动因素
[What makes this industry tick? 3-5 key factors]

---

## 行话速成:像内行人一样说话

| 术语 | 含义 | 使用场景 |
|------|------|----------|
| Term 1 | Meaning | When to use |
| Term 2 | Meaning | When to use |
| ... | ... | ... |

### 常用表达
- "[Insider phrase 1]" — 意思是...
- "[Insider phrase 2]" — 用于...

### 新手常犯的错误
- ❌ 不要说"..." → ✅ 应该说"..."

---

## 必知人物

### [Name 1] — [Title/Role]
> "[Famous quote]"

[Brief bio and why they matter]

### [Name 2] — [Title/Role]
...

---

## 经典案例

### 案例一:[Success/Failure Story Title]

**背景**: ...
**过程**: ...
**结果**: ...
**启示**: [Key takeaway in user's professional context]

![[media/case-example.jpg]]

---

## 精选视频片段

### [Video Title]
![[media/video-explanation.mp4]]
> 📌 **关键点**: [1-2 sentence summary of why this matters]

---

## 闪念卡片 (Flashcards)

🔮 点击展开卡片 1

**Q: [Question]**

---

**A: [Answer]**

[Generate 5-10 flashcards covering key concepts]

---

## 自测问答

### 问题 1
[Scenario-based question]

💡 查看答案

[Answer with explanation]

[Generate 3-5 quiz questions]

---

## 行动清单

基于你的目标「[user goal]」,建议的下一步:

- [ ] [Actionable item 1]
- [ ] [Actionable item 2]
- [ ] [Actionable item 3]

---

## 延伸阅读

- [Resource 1](url) — 推荐理由
- [Resource 2](url) — 推荐理由

---

> 💡 **学习小贴士**: [Personalized tip based on user's background]

5.2 Save Files

  1. 创建 media 文件夹
  2. 将所有媒体文件保存到 media 文件夹
  3. 保存主 markdown 文件

5.3 Scenario Modes (Output Adaptation)

根据用户目标自动选择输出重点(可显式声明 mode):

  • mode=networking:强调谈资、人物、热点话题
  • mode=interview:强调高频问答、结构化表达、案例拆解
  • mode=investment:强调商业模式、增长驱动、风险与竞争格局
  • mode=learning:强调概念体系、学习路径、记忆卡片

5.4 Output Quality Rubric (Self-check)

交付前按 1-5 分自评并简要补强薄弱项:

  • 清晰度(小白可理解)
  • 准确性(来源可信、可追溯)
  • 实用性(可用于会议/面试/投资交流)
  • 记忆性(闪卡与测验质量)
  • 可读性(结构、排版、图文对应)

若总分 = 18)

  • [ ] Personalization markers present throughout
  • [ ] Actionable next steps provided

Tool Integration

Required Tools

| Tool | Purpose | When to Use | |------|---------|-------------| | WebSearch | 搜索行业信息 | 每次必用 | | WebFetch | 获取网页详细内容 | 每次必用 | | zimage-skill | 生成概念图 | 概念性图片(流程图、价值链等) | | media-downloader | 下载图片和视频 | 事实性图片 + YouTube 视频 | | AskUserQuestion | 询问保存路径 | 保存前必用 |

zimage-skill (AI 图片生成)

功能: 使用 AI 生成概念图、流程图等

使用方式:

MODELSCOPE_API_KEY="your-key" python3 ~/.claude/skills/zimage-skill/generate.py "prompt" "output.jpg"

Prompt 最佳实践:

"[主题] diagram/infographic, minimalist style, [color] color scheme,
professional design, clean flat design, white background"

media-downloader (媒体下载器)

功能: 下载 YouTube 视频并裁剪

使用方式:

# 下载并裁剪视频
python ~/.claude/skills/media-downloader/media_cli.py youtube "URL" -o "目录" --end 120

# 检查配置状态
python ~/.claude/skills/media-downloader/media_cli.py status

Example Triggers

  • "帮我快速了解私募股权行业"
  • "I need to understand the semiconductor industry by next week"
  • "想成为咖啡行业的内行人"
  • "Help me master the basics of venture capital"
  • "下周要和动漫行业的人聊天,帮我速成"
  • "/chuinb blockchain"
  • "/master contemporary art"

Troubleshooting

zimage-skill 报错 "API Key required"

需要设置环境变量:

export MODELSCOPE_API_KEY="your-api-key"

获取 API Key: https://modelscope.cn/my/myaccesstoken

media-downloader 图片下载失败

需要配置图库 API Key:

export PEXELS_API_KEY="your-key"
export PIXABAY_API_KEY="your-key"

备选方案: 使用 zimage-skill 生成图片代替下载

YouTube 视频下载失败

确保已安装 yt-dlp:

pip install yt-dlp

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.