# Industry Research

AI Berkshire skill: 行业投资研究：产业链全景扫描 + 四大师个股分析框架.

- Repository: https://github.com/xbtlin/ai-berkshire (16972 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add xbtlin/ai-berkshire --skill industry-research -g -a claude-code -y`
Codex: `npx skills add xbtlin/ai-berkshire --skill industry-research -g -a codex -y`
Cursor: `npx skills add xbtlin/ai-berkshire --skill industry-research -g -a cursor -y`
Gemini CLI: `npx skills add xbtlin/ai-berkshire --skill industry-research -g -a gemini-cli -y`

## Description

AI Berkshire skill: 行业投资研究：产业链全景扫描 + 四大师个股分析框架. Source: skills/industry-research.md.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Codex adapter note

This skill is generated from `skills/industry-research.md` so Claude Code and Codex users share one canonical workflow.

- Treat `$ARGUMENTS` as the user's request in the current Codex thread.
- When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
- Use shared project tools from `tools/` in this repository. Prefer running commands from the repository root with paths like `python3 tools/financial_rigor.py ...`; if the current thread starts outside the repo, locate the actual checkout path first instead of assuming a fixed home-directory path.
- Before starting research, run the `date` command to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data.
- Preserve the research quality rules from `AGENTS.md`: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.

# 行业投资研究：产业链全景扫描 + 四大师个股分析框架

对 $ARGUMENTS 行业进行系统化产业链投资研究。

## 研究目标

从一个投资主题/逻辑链出发，完成：
1. 验证投资逻辑链的每一个环节
2. 绘制完整产业链全景图
3. 扫描全球所有上市公司（A股/港股/美股/国际）
4. 对每个细分环节的头部公司执行四大师框架分析
5. 输出行业级投资组合配置建议

---

## 第一步：投资逻辑链构建与验证

### 1.1 画出逻辑链
用箭头链路表达从"底层趋势"到"受益标的"的因果关系，例如：
```
底层趋势 A
    → 导致需求 B
        → 创造瓶颈/刚需 C
            → 受益产业链 D
```

### 1.2 逐环节验证
对逻辑链的每个箭头提出质疑并寻找证据：

| 环节 | 核心假设 | 验证方式 | 数据来源 |
|------|---------|---------|---------|
| A→B | | 搜索行业数据/预测 | |
| B→C | | 搜索供需分析 | |
| C→D | | 搜索实际案例/签约 | |

### 1.3 寻找"已发生的验证事件"
列出支撑该逻辑链的**已签约/已落地的真实商业事件**（而非预测），例如大公司的采购协议、政策文件、行业报告等。

---

## 第二步：产业链全景图绘制

### 2.1 绘制产业链结构
将行业拆解为上游→中游→下游→辅助环节，例如：
```
上游：原材料/资源开采 → 材料加工/提纯
中游：核心设备制造 → 系统集成/工程建设 → 新技术研发
下游：运营/服务 → 终端客户
辅助：检测/认证 → 维护服务 → 金融工具（ETF/信托）
```

### 2.2 识别每个环节的"生意特征"
对每个环节标注：

The rest: https://github.com/xbtlin/ai-berkshire/blob/HEAD/codex-skills/industry-research/SKILL.md