serenity-bottleneck-hunter

serenity-bottleneck-hunter is a skill for Codex from Mrjie7205/serenity-bottleneck-hunter. It costs 116 tokens per session (8,979 once invoked), scanned A, original, MIT.

A method for finding overlooked companies that supply a hard-to-replace part of an investment trend's supply chain. A supply-chain bottleneck is a limited upstream resource or supplier that can hold back the rest of the market.

In plain words
What is it for?
Mapping five supply-chain layers, screening suppliers and upstream companies, identifying cross-industry technology roots, and building a reasoned list of candidate stocks.
Why use it?
It helps investigate where demand must lead to spending and trace the trend backward before focusing on widely discussed companies. The results are for research and education, not investment advice.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Mapping five supply-chain layers, screening suppliers and upstream companies, identifying cross-industry technology roots, and building a reasoned list of candidate stocks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Mrjie7205/serenity-bottleneck-hunter --skill serenity-bottleneck-hunter
Clone the repo
git clone --depth 1 https://github.com/Mrjie7205/serenity-bottleneck-hunter

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for serenity-bottleneck-hunter

README.md
[![agentmods](https://agentmods.dev/badge/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter/github.svg)](https://agentmods.dev/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter)
Your own site
<a href="https://agentmods.dev/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter"><img src="https://agentmods.dev/badge/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for serenity-bottleneck-hunter

Your own site · 80×15
<a href="https://agentmods.dev/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter"><img src="https://agentmods.dev/badge/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,979 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00116 $0.08979
Opus 5.5 $0.00046 $0.03592
Sonnet 5.5 $0.00023 $0.01796
Haiku 4.5 $0.00012 $0.00898

Measured 1mo ago against content hash dc89644f5a56, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

serenity-bottleneck-hunter scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 11 executable files (scripts/build_release.py, scripts/init_ticker_truth.py, scripts/install_pre_commit.ps1, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl "https://eodhd.com/api/search/<COMPANY_NAME>?api_token=$KEY&fmt=json"
SKILL.md · 239 lines

How it starts

The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Serenity Bottleneck Hunter

把一个投资主题转成一份被忽视的上游瓶颈候选股名单 + 论证 + 目标价/时间框架。核心不是抄 Serenity 的票,而是复用他的逻辑去选新股。

📓 每条纪律是怎么用一次真实翻车换来的,见 reference/lessons.md。本文件只讲"做什么";完整事故复盘在那边,用 〔… → lessons.md#锚点〕 指过去。

何时用

  • 用户给出一个主题/趋势,想要"沿这个方向能买什么"。
  • 用户给一个上游环节/材料,想找对应的瓶颈公司。
  • ❌ 不要用于:已知标的的纯财务分析、抄作业式"他买了啥"。

核心理念

逆向拆解供应链,在机构与分析师发现之前,埋伏那个无人察觉的上游瓶颈,用催化剂兑现。 alpha 来自"早于机构发现主题",不是抄到最低点。


v0.2 工具入口

  • 首次安装后可执行 python scripts/run_demo.py:不联网的历史演示,输出与正式跟踪表分开;说明见 examples/README.md。演示不替代真实研究。
  • 实际 A 股/美股/港股取价与估值设置见 reference/DATA_SOURCES.md。保留来源、行情日期及质量提醒;不得按跳空幅度自行猜除权。
  • 报告 SPEC 中相对路径从本技能根目录解析;可用 tracking_file 选择自己的 CSV。现有默认路径兼容保留。shell_from 缺省时使用内置模板,无需私库或驾驶舱。
  • 发布前可用 python scripts/verify_report.py <报告> --scan <输入> --tracking <跟踪表> --strict 完整对账。严格模式的输入缺失和数字错配必须先解决。

可选驾驶舱

只有用户需要管理、跟踪或复盘已有研究时,才使用 cockpit/README.md 的本地驾驶舱。核心研究流程不依赖它。

默认 python cockpit/run.py 读取只读历史演示,不抓取行情。用户自己的研究须明确指定目录;报告 SPEC 的 out 与 tracking_file 应指向该目录。取价、告警核对和定时任务分别由用户手动触发或显式启用。驾驶舱的“猎手/覆盖”是显示范围,不是龙头视角。

个人目录的星级用 tracking/cross_theme_scan.py --input <个人跟踪表> --output <个人星级表> 重建,避免把公库历史样本的星级混入个人研究。

工作流(7 步)

Step 1 · 确认资本开支确定性 这个主题的钱为什么"一定"会花?规模、周期多长?需求确定性 > 个股故事性。先归类:Bottleneck(瓶颈)/ Disruption(颠覆)/ Evolution(演进)?(本 skill 主攻 Bottleneck)

  • 标明需求来源:政府/国防(节奏慢、看订单与预算周期)还是商业(超大规模厂商 capex、节奏快)?二者估值锚与择时节奏不同,Step 6/7 据此调整。

Step 2 · 逆向拆链 + 广扫纪律(5 层产业链 + 跨主题 root + 可选第 6 层独有 IP) 列出 5 层 从下游到上游:下游对照 → 中游系统 → 中游器件 → 上游设备 → 上游材料/代工。跳过人人都盯的下游龙头。

  • 必做"广扫供应商":除最上游材料层外,单独再搜一轮该主题的子系统/器件/卖铲子供应商,否则漏 ④ 原型标的。〔教训:商业航天漏 Redwire → lessons.md#redwire〕

  • 广扫颗粒度:覆盖 5 层,每层至少 2-3 个标的。广扫不到位 ≠ 板块没机会 —— 停手前先问"是真没机会,还是没扫够"。〔教训:800VDC 6 只→15 只才挖出 STM → lessons.md#breadth-stm〕

  • 跨主题 root 节点:ARM / CDNS / SNPS 放在 chain-viz 上方独立的"跨主题 root"小卡片,显示一次,不画进每个主题的产业链层(三家是所有芯片设计驱动主题共用的根)。〔教训:7 份报告 IP/EDA 层 100% 重复 → lessons.md#ip-eda-root〕

    <div class="cross-theme-root">
      <span class="label">跨主题 root(芯片设计驱动主题共用)</span>
      ARM ⭐⭐ + CDNS ⭐⭐ + SNPS ⭐⭐
    </div>
    

    其 ⭐⭐ 状态来自 tracking/cross_theme_index_snapshot.csv,不每个主题重画。

  • 可选第 6 层 · 主题独有 IP/EDA 玩家(仅当主题真有独立玩家才画):

    • 自动驾驶 L4 → 真有独立 IP:MBLY(Mobileye 授权给 BMW/Audi/Geely)— 必须画
    • 个人 AI PC → ARM 在 PC 是真单源(Apple M + Qualcomm Snapdragon X + AMD AI300)— 保留画在产业链顶层 + 标本主题真单源
    • AI Agent / 物理 AI / AMR → 无独立 IP 玩家 → 不画第 6 层,root section 提一次即可
    • MLCC / 800VDC / 商业航天 → 不依赖芯片设计,完全跳过 IP/EDA
    • 凑数禁令:没有真独立玩家的主题不强制画第 6 层 —— IP/EDA 是 optional 不是 mandatory。

Read the full file on GitHub · 239 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +15 lines dc89644f5a56
  2. 2mo ago First seen · 224 lines · 116 tokens per session scan A a3cc6a793f34

Subscribe to this mod's changes

serenity-bottleneck-hunter is a skill published in the GitHub repository Mrjie7205/serenity-bottleneck-hunter (412 stars, last pushed 1mo ago), licensed MIT. It adds 116 tokens to every session and 8,979 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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