# Supply Chain Bottleneck Finder

Finds global supply chain bottlenecks that create arbitrage openings across industries.

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

## Install

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

## Description

AI Berkshire skill: 供应链瓶颈猎手：AI驱动的全球产业链瓶颈套利. Source: skills/bottleneck-hunter.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/bottleneck-hunter.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.

# 供应链瓶颈猎手：AI驱动的全球产业链瓶颈套利

对 $ARGUMENTS 超级趋势执行供应链瓶颈扫描与套利机会挖掘。

## 核心理念

不问"AI推荐什么股票"，问"如果这个趋势继续扩张，哪一环会先不够用？"

传统投研盯龙头、盯已知赛道。这个系统反过来：**从物理供应链的咽喉位置出发，找那些没人注意但一旦断货整个行业都得停下来等的公司**。

超额收益来源：第一层瓶颈（GPU、HBM、电力）已被充分定价。真正的alpha在**第二层、第三层**, , 光模块、激光器、InP衬底、SOI晶圆、外延设备、晶圆级测试、IC载板、特殊玻纤等。

---

## 第一步：超级趋势确认

### 1.1 趋势筛选标准

不在小风口里找幻觉，只追符合以下全部条件的超级趋势：

| 标准 | 要求 | 验证方法 |
|------|------|---------|
| 持续性 | 至少3-5年确定性增长 | 搜索行业预测、资本开支计划 |
| 物理性 | 需要实际硬件/材料/设备建设 | 区分"软件升级"和"物理扩建" |
| 规模性 | 全球资本开支>500亿美元/年 | 搜索头部玩家capex指引 |
| 加速性 | 需求增速>供给扩产速度 | 对比需求增长率vs产能扩张计划 |

### 1.2 当前跟踪的超级趋势清单

每次运行时更新，初始清单：

1. **AI基础设施建设**, 数据中心、GPU集群、网络互联、电力
2. **能源转型**, 核电重启、电网升级、储能
3. **国防现代化**, 西方军费上升周期、供应链重构
4. **半导体再工业化**, 美欧日补贴建厂、设备/材料瓶颈
5. **太空经济**, 卫星互联网、发射频次激增

如果用户指定了具体趋势（如"AI基础设施"），只聚焦该趋势。

### 1.3 趋势验证输出

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