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.
npx skills add Mrjie7205/serenity-bottleneck-hunter --skill serenity-bottleneck-huntergit clone --depth 1 https://github.com/Mrjie7205/serenity-bottleneck-hunterWrote 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.
[](https://agentmods.dev/skills/mrjie7205/serenity-bottleneck-hunter/serenity-bottleneck-hunter)<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.
<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>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.
| Model | Per session | Once 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 | $0.00898 |
Grade A, and why
serenity-bottleneck-hunter scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl "https://eodhd.com/api/search/<COMPANY_NAME>?api_token=$KEY&fmt=json" 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。
What ships with it
19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 266 B
- examples/blank_tracking.csv 119 B
- examples/demo_scan.json 901 B
- examples/demo_spec.json 5.3 KB
- examples/demo_tracking.csv 489 B
- examples/demo-report.html 40 KB
- examples/provenance.json 526 B
- examples/README.md 1.5 KB
- scripts/build_release.py 5.5 KB runs code
- scripts/init_ticker_truth.py 3.7 KB runs code
- scripts/install_pre_commit.ps1 394 B runs code
- scripts/install_pre_commit.py 1.9 KB runs code
- scripts/price.py 13 KB runs code
- scripts/render_report.py 16 KB runs code
- scripts/run_demo.py 1.4 KB runs code
- scripts/theme_etf_coverage.py 5.5 KB runs code
- scripts/ticker_truth.py 9.5 KB runs code
- scripts/verify_report.py 20 KB runs code
- scripts/verify_tickers.py 18 KB runs code
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.
- 1mo ago Changed · +15 lines dc89644f5a56
- 2mo ago First seen · 224 lines · 116 tokens per session scan A a3cc6a793f34
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.
serenity-bottleneck-hunter compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| serenity-bottleneck-hunterMrjie7205/serenity-bottleneck-hunter | Skill | 412 | 1mo ago | MIT | A | 116 | Codex |
| sector-rotationHKUDS/Vibe-Trading | Skill | 38k | yesterday | MIT | A | 39 | Claude Code, Codex |
| twitter-readerhimself65/finance-skills | Skill | 3.5k | 2d ago | MIT | A | 110 | Claude Code |
| strategy-pivot-designertradermonty/claude-trading-skills | Skill | 3.7k | 2d ago | MIT | A | 28 | Claude Code, Codex |
| energy-procurementhashgraph-online/awesome-codex-plugins | Skill | 1.5k | yesterday | Apache-2.0 | A | 93 | Claude Code, Codex |
| airdrop-hunting-guidenirholas/three.ws | Skill | 269 | today | Apache-2.0 | A | 54 | Claude Code, Codex |
| multi-expert-analyzerdigoal/blog | Skill | 8.6k | 2d ago | GPL-2.0 | A | 292 | Codex |