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 cyijun/china-financial-services --skill a-share-company-underwritinggit clone --depth 1 https://github.com/cyijun/china-financial-servicesWrote 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/cyijun/china-financial-services/a-share-company-underwriting)<a href="https://agentmods.dev/skills/cyijun/china-financial-services/a-share-company-underwriting"><img src="https://agentmods.dev/badge/skills/cyijun/china-financial-services/a-share-company-underwriting/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/cyijun/china-financial-services/a-share-company-underwriting"><img src="https://agentmods.dev/badge/skills/cyijun/china-financial-services/a-share-company-underwriting.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 | $0.00073 | $0.00671 |
| Opus 5.5 | $0.00029 | $0.00268 |
| Sonnet 5.5 | $0.00015 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
a-share-company-underwriting 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 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.
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.
What it actually says
A股公司承保式研究
目标是回答“公司靠什么赚钱、为何能持续、什么会使判断失效”，不是堆积公司介绍。
工作流
- 先调用
a-share-research-evidence固定时点和证据范围；关键事实缺少原始材料时降级结论。 - 用五行拆解每项重要业务：卖什么、谁付钱、如何触达、如何定价、收入是否重复发生。
- 建立盈利驱动树：销量/客户数×单价/ARPU×变现率，连接毛利、费用、资本开支、营运资本和自由现金流。
- 研究价值链、利润池、集中度、产能周期、进入壁垒、替代路径、政策和行政定价。TAM必须声明口径。
- 每个“护城河”必须命名机制，并用价格、份额、毛利率、ROIC、获客投入或留存验证方向。
- 评估承诺兑现、资本配置、股权质押、关联交易、激励、审计意见、关键人和继任。以行为记录为主。
- 构造基础、压力和乐观运营情景，列驱动、可观察触发器和范围，不赋主观概率。
- 使用
china-market-data获取财务、估值、行业成员和行情；定性结论仍需公告、年报和产业证据。
五行拆解结构改写自prof-little-bear/cc-equity-research的community-skills/analyze/business-model.md（检视commit 7c428944a6718c35461f839c618ae66334b6371b，Apache-2.0）；本版本将证据基准替换为中国年报、招股书、问询函及交易所材料。归属不代表上游对本项目背书。
硬约束
- 不用份额、品牌或管理层声誉单独证明护城河。
- 不把历史增长直线外推，不用综合星级掩盖否决项。
- 银行、保险、券商、地产、资源品和早期成长公司必须使用专门口径。
- 只输出研究，不给买入、卖出或仓位指令。
输出契约
按结论摘要、商业模式、盈利驱动、行业结构、竞争优势、治理、情景、风险与未知、后续验证输出，并区分事实、推断和待验证项。
需要结构模板时读取references/underwriting-schema.md。
What ships with it
1 file 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.
- 1mo ago First seen · 35 lines · 73 tokens per session scan A 5d316c3f2603
a-share-company-underwriting is a skill published in the GitHub repository cyijun/china-financial-services (21 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 671 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
a-share-data
A data lookup skill for mainland Chinese A-shares, meaning stocks traded on China's Shanghai and Shenzhen exchanges. It covers prices, history, indicators, company events, sectors, concepts, market news, and macroeconomic data.
a-share-paper-trading
A simulated stock-trading and backtesting system for mainland Chinese A-shares. Simulated trading uses pretend orders and money, while backtesting evaluates a strategy against past data.
a-share-strategy-allmarket-multi-swing-defensive
A stock-market decision aid for liquid stocks across China's full market. It uses a trend-pullback strategy to produce possible buy and sell signals and can fetch current price snapshots, but it does not backtest or place orders.
macd-second-golden-cross
A trading-analysis guide for Chinese A-share stocks using MACD, an indicator built from moving averages to show momentum. It focuses on a bullish divergence followed by a second crossover below the zero line.
tuige-shortline-trading
A Chinese-language rulebook for short-term stock trading in the Chinese A-share market. It organizes market conditions, stock selection, chart setups, exit rules, and position discipline.
通达信TQ
A connection between Python strategy files and TdxQuant, TongdaXin's platform for securities data analysis and quantitative investment research. It uses the platform's tqcenter.py module to interact with the TongdaXin client.