bottleneck-hunter

## 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 ##...