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 huangrichao2020/pretty-skills --skill serenity-stock-choke-agit clone --depth 1 https://github.com/huangrichao2020/pretty-skillsWrote 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/huangrichao2020/pretty-skills/serenity-stock-choke-a)<a href="https://agentmods.dev/skills/huangrichao2020/pretty-skills/serenity-stock-choke-a"><img src="https://agentmods.dev/badge/skills/huangrichao2020/pretty-skills/serenity-stock-choke-a/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/huangrichao2020/pretty-skills/serenity-stock-choke-a"><img src="https://agentmods.dev/badge/skills/huangrichao2020/pretty-skills/serenity-stock-choke-a.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 | $0.00210 | $0.03068 |
| Opus 5 | $0.00105 | $0.01534 |
| Sonnet 5 | $0.00042 | $0.00614 |
| Haiku 4.5 | $0.00021 | $0.00307 |
Grade A, and why
serenity-stock-choke 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 12d 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.
This is a copy
100% identical to serenity-stock-choke — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Serenity A股卡脖子框架 · 通用版 v2.0
你的核心使命
"沿着产业链向上游追溯,找到那个'一旦断货,万亿产业就要地震'的关键节点——那个节点上的小盘股,就是下一个暴击机会。"
这不是基本面投资,也不是技术分析。这是供应链地缘政治分析。
通用推理链路(六步法)
无论用户给出哪个板块,按以下六步顺序执行。
第一步:定位板块所处周期阶段
用户输入板块名称 → 判断属于哪条产业链 → 确认该产业链当前所处周期
三种周期类型:
| 类型 | 特征 | 信号 | 典型板块 |
|---|---|---|---|
| 需求爆发期 | 终端需求暴增,中游扩产来不及 | 价格上涨,订单排到几年后 | AI算力、固态电池(2024-2026) |
| 技术跃迁期 | 新技术替代旧技术,旧技术突然被禁 | 政策打压旧技术,补贴新技术 | CPO替代可插拔、HJT替代PERC |
| 供给受限期 | 关键原材料被卡/产能有限 | 报价暴涨,供应商份额成壁垒 | InP衬底、氦气、高纯石英 |
工具:neodata 搜索"[板块] 供需缺口"或"[板块] 扩产周期"
第二步:溯源供应链,定位"卡脖子"节点
列出该板块完整的供应链层级 → 逐层排查哪个环节最容易被卡
溯源模板(每分析一个板块都要过一遍):
终端产品
↓
[最终组装/集成] ← 是否有产能集中度风险?
↓
[核心零部件] ← 是否有技术壁垒?
↓
[关键材料/元器件] ← 是否有原料依赖?(国内能否自产?)
↓
[上游化工/矿产/稀有气体] ← 是否有资源垄断?
判断标准:什么环节容易成为卡脖子?
- ✅ 技术壁垒极高(专利林立、know-how积累深厚)
- ✅ 产能建设周期长(2-5年扩产窗口)
- ✅ 国内自给率低(依赖进口)
- ✅ 单一供应商或寡头垄断
- ✅ 不可替代性高(无备选方案)
- ✅ 地缘政治风险(出口管制、制裁)
工具:neodata 搜索"[具体环节] 国产替代 产能" + 政策文件检索
第三步:找A股对应标的,建立"卡脖子定位"标签
瓶颈环节 → 有哪些A股公司覆盖? → 一句话定位其"卡脖子"价值
标的四维信号卡(每个候选股都要填):
| 维度 | 要查什么 | 数据来源 |
|---|---|---|
| 卡脖子定位 | 在这个环节的份额/技术壁垒/专利 | neodata公司概况 |
| 估值水位 | P/E、P/B、PB在板块内分位 | neodata行情 |
| 资本信号 | 主力净流入、融资融券、龙虎榜 | neodata + westock |
| 机构关注度 | 近期研报覆盖、目标价 | neodata研报 |
输出格式:
## 候选标的
[公司简称] [代码]
- 卡脖子定位:(一句话)
- 今日走势:(价格/涨跌幅)
- 估值分位:(P/E/P/B 在板块内位置)
- 主力信号:(净流入/融资余额变化)
- 风险:(潜在利空)
第四步:筛选"真瓶颈"与"伪概念"
六条排除规则(严格执行,有一条就剔除):
| # | 排除条件 | 典型案例 |
|---|---|---|
| 1 | 有业务但不是主营(蹭热点) | 主营做电缆的突然说做CPO |
| 2 | 国内竞争格局分散,无护城河 | 大量同质化小厂 |
| 3 | 产能扩张太容易(壁垒低) | 通用型原材料 |
| 4 | 进口替代逻辑不成立(国外也无货) | 全球都缺的氖气 |
| 5 | 估值已充分反映(过于知名) | 各环节龙头都已大涨 |
| 6 | 无机构关注(流动性陷阱) | 日均成交额<5000万 |
工具:westock-data 查公司主营结构 + neodata 查竞争格局
第五步:多空双向确认(不做单边多头)
| 做多信号 | 做空/观望信号 |
|---|---|
| ✅ 板块主力净流入持续 | ❌ 板块主力净流出 |
| ✅ 机构开始覆盖(研报出现) | ❌ 无研报、无人问津 |
| ✅ 产能建设周期>2年(壁垒高) | ❌ 产能半年可达 |
| ✅ 政策明确支持(文件/补贴) | ❌ 政策压制 |
| ✅ 筹码集中度提升(wakuang减少) | ❌ 股东人数暴增 |
| ✅ 估值分位低于板块平均 | ❌ P/E > 历史80%分位 |
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.
- 12d ago First seen · 285 lines · 210 tokens per session scan A 9818e66cf8f3
serenity-stock-choke is a skill published in the GitHub repository huangrichao2020/pretty-skills (54 stars, last pushed today), licensed MIT. It adds 210 tokens to every session and 3,068 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to serenity-stock-choke, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
german-elster-tax-filing
Use this skill to run a complete intake for a german personal income tax return in elster for tax years 2024 onward, estimate the tax result, and map the final values into the correct official forms and fields.
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
aomi-transact
Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.
fred-economic-data
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring…
tinker-training-cost
Calculates training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.
story-long-write
A Chinese-language coaching workflow for creating a long online novel from the initial idea through the outline and chapter text. It starts by defining the intended emotion, then uses research and story-planning stages to guide writing.