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-lbo-modelgit 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-lbo-model)<a href="https://agentmods.dev/skills/jwangkun/claude-for-financial-services-cn/china-lbo-model"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-lbo-model/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-lbo-model"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-lbo-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00064 | $0.02399 |
| Opus 5 | $0.00032 | $0.01200 |
| Sonnet 5 | $0.00013 | $0.00480 |
| Haiku 4.5 | $0.00006 | $0.00240 |
Grade A, and why
china-lbo-model 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
china-lbo-model
Purpose
Build institutional-quality LBO models for transactions in the China market, accounting for:
- Chinese debt market structures (bank loans, 公司债, 中期票据)
- CAS accounting standards
- A-share trading restrictions and considerations
- China-specific tax treatment
Key Differences from US LBO Models
| Parameter | US LBO | China LBO |
|---|---|---|
| Debt market | Public bonds, institutional loans | Bank syndicated loans, 公司债, ABS |
| Typical leverage | 5-7x EBITDA | 3-5x EBITDA (bank-driven) |
| High-yield market | Developed | Limited (mostly onshore/offshore 高收益债) |
| Covenant package | Bond covenants, bank covenants | Primarily bank-led covenants |
| Management roll | Standard | Often 管理层持股 + earn-out |
| Tax rate | 21% federal | 25% standard (高新技术企业 15%) |
| Currency | USD | CNY (人民币) |
| Accounting | US GAAP / IFRS | CAS (企业会计准则) |
Data Sources
Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)
get_financials(ticker, "income", "annual") → 利润表 (EBIT, Net Income)
get_financials(ticker, "balance", "annual") → 资产负债表 (Debt, Cash)
get_financials(ticker, "cashflow", "annual") → 现金流量表 (FCF, CapEx)
get_quote(ticker) → Current valuation
get_historical_data(ticker) → Trading history
get_stock_info(ticker) → Company profile
Secondary Sources
- 巨潮资讯 — public filings for historicals
- 公司公告 — debt agreements, covenant packages
- 银行间市场交易商协会 — bond issuance data
- Wind / 同花顺 — comparable transaction multiples
Workflow
Step 1: Company & Transaction Setup
Transaction Structure (China-specific):
- Buyer type: 战略投资者 (strategic) vs 财务投资者 (financial sponsor)
- ** Listed status**: A-share delisting process vs 借壳上市 (reverse merger)
- Deal structure: 股权收购 vs 资产收购 vs 吸收合并
- Lock-up: 限售期 considerations (typically 12-36 months)
Entry Valuation:
- Current market cap (流通市值 or 总市值)
- Control premium benchmark: 20-40% typical for China M&A (vs 20-30% US)
- Take-private premium for A-share delistings: 30-50%
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 · 274 lines · 64 tokens per session scan A c7f3725d483e
china-lbo-model is a skill published in the GitHub repository jwangkun/claude-for-financial-services-cn (745 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 2,399 once invoked, about $0.0003 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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.