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 jwangkun/claude-for-financial-services-cn --skill china-deal-screeninggit clone --depth 1 https://github.com/jwangkun/claude-for-financial-services-cnWrote 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/jwangkun/claude-for-financial-services-cn/china-deal-screening)<a href="https://agentmods.dev/skills/jwangkun/claude-for-financial-services-cn/china-deal-screening"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-deal-screening/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/jwangkun/claude-for-financial-services-cn/china-deal-screening"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-deal-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00083 | $0.01723 |
| Opus 5 | $0.00042 | $0.00861 |
| Sonnet 5 | $0.00017 | $0.00345 |
| Haiku 4.5 | $0.00008 | $0.00172 |
Grade A, and why
china-deal-screening 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 13d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
china-deal-screening
Purpose
Screen A股投资标的 — systematic deal screening for Chinese equity investments.
Data Sources
Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)
get_industry_stocks(industry="...") → Industry universe
get_quote(ticker) → Valuation screening
get_financials(ticker, "income") → Financial screening
get_index_data("000001") → Market context
Secondary Sources
- 东方财富 — stock screener
- 同花顺 iFinD — screening tool
- 巨潮 — company filings
- Wind — professional screening
Workflow
Step 1: Define Screening Criteria
Screening framework:
| Category | Criteria | Typical Range |
|---|---|---|
| 市值 (Market cap) | Min/max | ¥50亿 - ¥500亿 |
| 估值 (Valuation) | P/E, P/B | P/E 10-30x |
| 成长 (Growth) | Revenue/earnings growth | >15% YoY |
| 盈利 (Profitability) | ROE, margins | ROE >15% |
| 财务健康 (Financial health) | Debt/equity, current ratio | D/E <60% |
| 流动性 (Liquidity) | Avg daily volume | >¥5000万 |
| 治理 (Governance) | Ownership structure | Clean cap table |
Step 2: Build Screening Universe
Universe construction:
| Filter | Criteria | Source |
|---|---|---|
| A股主板 | 600/000/001开头的6位代码 | AkShare |
| 创业板 | 300开头 | AkShare |
| 科创板 | 688开头 | AkShare |
| 北交所 | 8/9开头 | AkShare |
| ST排除 | Exclude ST/*ST | Filter |
| 次新股 | Exclude <6 months | Filter |
Step 3: Financial Screening
Financial metrics:
| Metric | Formula | Target |
|---|---|---|
| 营业收入增速 | (Revenue - Revenue_prev) / Revenue_prev | >15% |
| 净利润增速 | (NI - NI_prev) / NI_prev | >15% |
| ROE | Net Income / Average Equity | >15% |
| 毛利率 | Gross Profit / Revenue | >30% |
| 净利率 | Net Income / Revenue | >10% |
| 资产负债率 | Total Debt / Total Assets | <60% |
| 经营现金流/净利润 | OCF / Net Income | >0.8 |
Step 4: Valuation Screening
Valuation metrics:
| Metric | Formula | Target |
|---|---|---|
| P/E (TTM) | Price / TTM EPS | 10-30x |
| P/B | Price / BV per share | 1-5x |
| P/S | EV / Revenue | 1-5x |
| EV/EBITDA | EV / EBITDA | 5-15x |
| PEG | P/E / Growth rate | <1.0 |
| 股息率 | Dividend / Price | >2% (if applicable) |
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.
- 13d ago First seen · 213 lines · 83 tokens per session scan A f588928992c3
china-deal-screening is a skill published in the GitHub repository jwangkun/claude-for-financial-services-cn (744 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,723 once invoked, about $0.0004 per session on Opus 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.
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