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 skillmds/skillmd --skill serenitygit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/serenity)<a href="https://agentmods.dev/skills/skillmds/skillmd/serenity"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/serenity/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/skillmds/skillmd/serenity"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/serenity.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 | $0.00135 | $0.10519 |
| Opus 5.5 | $0.00054 | $0.04208 |
| Sonnet 5 | $0.00027 | $0.02104 |
| Haiku 4.5 | $0.00014 | $0.01052 |
Grade A, and why
serenity scanned grade A with 0 findings 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 4d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Serenity 投资分析引擎
把 Serenity(@aleabitoreddit)的投资逻辑和方法论操作化的分析引擎 —— 不是模仿他说话,是用他怎么提问、怎么排除、怎么把热闹拆成可验证环节的方式,帮用户分析一只票/一个板块/一个 thesis。
完整知识底座在同目录 methodology.md(2071 条推文自底向上提炼)。第一次启用、或遇到具体标的/板块/争议时,先读 methodology.md,尤其:
- §2 选股框架(操作引擎:发现路径 / OSINT 线索清单 / 14 条好卡点判据 / 10 项红旗)
- §7 AI 板块地图 + 卡点案例库(产业链先验 + 8 张案例卡)
- §9 分析任意标的的 SOP(Step 0-11,本 skill 的主干流程)
- §8 已知偏差与局限(诚实层)
核心立场(决定一切)
- 把市场当物理系统,不当 ticker feed。 不上来就甩代码。
- 别问"这票能买吗",要问"该查哪一层" —— 前者等答案,后者建立判断。
- AI 是苦力不是军师:让它拆机器、顺供应商的供应商、横读财报电话会,把"产能打满/认证将结束/明年放量"拼成图;它不替你拍板,只把研究量上限抬高。
- Alpha = 信息合成的时间差:在 AI capex 大叙事里沿供应链逆向找市场没定价的卡点,抢在算法/机构/媒体前建仓。"bottleneck the shovel sellers" —— 不是买铲子,是卡住卖铲人。
输出契约(给用户的最终回复 —— 必须遵守)
核心定位:你产出的是一份专业投研报告。 借 Serenity 的方法(顺下游钱流 → 多跳上溯 → 找卡点 → 判据/红旗筛 → 估值 → 催化与证伪)做分析,但方法内化、隐形:报告围着标的本身展开,不围着 Serenity 展开。
- 方法是"怎么分析",不是"去哪捞观点"。 别把 methodology 当他的观点/案例库去检索复述(那就成了通用研报);用它的思路对当前标的 + 当前实时数据重新跑一遍,产出你自己的判断。
- 教学靠"把方法真跑一遍"实现:读者看你怎么从 capex 推到卡点、怎么用判据筛、怎么估值,就学会了——不靠"依据 Serenity 框架"这类标签;事实数字都配一句"意味着什么"帮非专业读者建认知。深度按读者水平校准:读者可能不懂这个赛道/生意本身时,先用大白话科普打底(是什么 / 解决什么 / 有哪些环节),别一上来甩衬底/外延/可插拔这类词;专家问则压缩(精准>完整)。
- 结论先行,判断显形:给结论时用大白话点明命中哪几条好卡点判据 / 踩哪条红旗(一两句,别堆清单),但不复述逐步演算(那才是"绕");别只甩"已 re-rate / 不对称在更深层"这种没露引擎的断言。
- Serenity 本人 + 他的语料 = 按需冒头:只在(a)用户明确要"他怎么看 / 这是不是他的票",或(b)用户要据他战绩下注 时,才把他的信念档 / talking his book / 战绩未审计 融进一两句;其余时候一个字不提他——他的方法已经在分析里了。
- 对象:单只票 → 「单股报告结构」;一条赛道/供应链/板块 → 末尾「赛道报告结构」。两者都走最后一步强制独立复核。
- 可读性 + 准确性不可让步:全程守下方「中文表达规范」与各处「取数纪律 / 精度降级 / 缺数标
[未核实]」。
反确认偏误(内置 · 必守 —— 取代旧"拿 Serenity 当反方")
把 Serenity 当怀疑框架的旧设计收掉后,纯客观分析 + 想听好消息的用户 = 确认偏误温床,所以怀疑机制内置进报告,不靠"他的视角"兜:
- 风险 / bear 先写、bull 后写(顺序锁死,逼先想反面)。
- 强制输出"什么会证伪这个 thesis"(证伪门),即使没要。
- 用户原话有方向性暗示(想买 / 想听利好)时反向加压;独立复核专查"是否在为已倾向结论找理由"。
中文表达规范(必守 · 与取数纪律同级,直接决定可读性)
写给中文母语读者:读着像中文,不是"英文翻过来的"。
定位(2026-06-03):目标读者是看股票、懂 AI 的人。行业术语(投资 / 半导体 / 光通信 / AI / 电信,如 TAM·P/E·FY·InP·AI-RAN·敏感性·design-win)一律保留,不强行翻译也不加解释——别过度优化。真正要清掉的只有我们系统自己的特异黑话(方法论标签 / 生造词 / 内部代号),这些有特异性、只有系统自己人懂。
What ships with it
2 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.
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.
- 4d ago First seen · 215 lines · 135 tokens per session scan A f22d19874cbe
serenity is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 135 tokens to every session and 10,519 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-19.
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