Given an investment theme, this skill reverse-maps the supply chain to surface overlooked upstream "bottleneck" stocks — distilled from the publicly shared methodology of X/Twitter trader Serenity (@aleabitoreddit).

给定一个投资主题,复用 X 博主 Serenity 公开分享的"供应链瓶颈逆向映射"方法,独立挖出被市场忽视的上游瓶颈股(而非抄他已喊过的票)。

• Not financial advice — educational / research use only. / 非投资建议,仅供研究教育。
• This project distills a methodology from Serenity's public posts. It does not redistribute his content and is not affiliated with, sponsored by, or endorsed by him. / 本项目只提炼其公开方法论,不转载其原始内容,与本人无任何关联或背书。
• Any "validation" inside is logic-consistency + forward tracking, NOT an audited performance record. Markets are risky; do your own research. / 文中"验证"为逻辑自洽 + 向前跟踪,非经审计的业绩。投资有风险,务必独立判断。

Theme → reverse supply-chain map → apply 9 "bottleneck archetypes" → output a short list of overlooked upstream candidates with: thesis, archetype, valuation, entry-timing (Mode-A "buy early momentum, not the dip"), target/timeframe, and risks. The edge is being early to the theme, not chasing crowded names.

主题 → 逆向拆解供应链 → 套用 9 大瓶颈原型 → 产出被忽视的上游候选 + 论点/估值/入场时机/目标价/风险。核心是早于机构发现主题,不追拥挤标的。

• As a Claude skill: drop this folder into your skills directory (or install the .skill bundle), then ask Claude e.g. "用 Serenity 的方法分析 AI 数据中心电力 这个主题,给候选标的".
• Or just point Claude at SKILL.md.

SKILL.md                         # 主流程:主题→挖股 7 步 + 两套择时 + 输出模板
reference/
  methodology.md                 # 方法论(理念/筛选清单/两套择时/回避清单/风险)
  supply-chain-and-archetypes.md # 元框架 + 产业链速查表 + 9 大瓶颈原型库
  example_commercial_space.md    # 完整 worked example(商业航天)
scripts/price.py                 # 价格/动量助手(EODHD 优先 → yfinance 兜底,绝不用 WebSearch 猜)
tracking/                        # 向前(样本外)验证:候选表 + 打分脚本

• Price & timing: scripts/price.py 自动按 EODHD(EODHD_API_KEY)→ yfinance 顺序回退。EODHD 全球覆盖最广(海外股推荐);yfinance 无需 key,美股 OK 但非美股常有 gap。WebSearch 一律不用于抓价格——猜测视为流程错误。
• Fundamentals & bottleneck judgment: web research per candidate — the skill's real edge is qualitative (is it a true single-source chokepoint?), which no data feed provides.

The only credible test is forward / out-of-sample: see tracking/forward_picks.csv (dated, rules-locked picks) + tracking/score_tracker.py (re-pulls prices later and scores them). Any in-sample "backtest" suffers look-ahead & survivorship bias and is not a performance claim.

MIT (see LICENSE). Methodology credit: Serenity (@aleabitoreddit) — this is an independent, fan-made distillation of publicly shared ideas.