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-returns-analysisgit 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-returns-analysis)<a href="https://agentmods.dev/skills/jwangkun/claude-for-financial-services-cn/china-returns-analysis"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-returns-analysis/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-returns-analysis"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-returns-analysis.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 | $0.00080 | $0.01751 |
| Opus 5.5 | $0.00032 | $0.00700 |
| Sonnet 5.5 | $0.00016 | $0.00350 |
| Haiku 4.5 | $0.00008 | $0.00175 |
Grade A, and why
china-returns-analysis 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.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
china-returns-analysis
Purpose
Analyze and report on 中国私募基金回报表现 using IRR, MOIC, and related metrics.
Key Metrics
Core Metrics (Same as Global)
| Metric | Formula | Benchmark |
|---|---|---|
| IRR (Internal Rate of Return) | Discounted cash flow rate | 15-20%+ for top China PE |
| MOIC (Multiple on Invested Capital) | Total returned / Total invested | 2.0-3.0x typical |
| TVPI (Total Value to Paid-In) | (Distributed + NAV) / Paid-in | >2.0x target |
| DPI (Distributed to Paid-In) | Distributions / Paid-in | 0.5-1.0x early stage |
| RVPI (Residual Value to Paid-In) | NAV / Paid-in | >1.0x for active funds |
China-Specific Metrics
| Metric | Description | Benchmark |
|---|---|---|
| 人民币IRR | Returns in CNY | 12-18% for quality funds |
| 美元IRR | Returns in USD (hedged/unhedged) | 15-25% |
| 项目回报 | Per-investment IRR/MOIC | 20-30% IRR target |
| 基金回报 | Fund-level IRR | Top quartile: 20%+ |
| 现金回款 | Distributions received | Annual yield target |
Data Sources (Multi-Tier)
Tier 0 — 万得 Wind（最全面付费数据）
- 覆盖：A股/港美股/基金/指数/债券/宏观/研报/分析（44个工具）
- MCP 服务：
wind-mcp（需WIND_API_KEY密钥，以ak_开头） - 优势：全市场覆盖面最广、数据最全面、包含研报和量化分析
- 密钥申请：https://aifinmarket.wind.com.cn/#/home
Tier 1 - iFind (paid)
When IFIND_AUTH_TOKEN is configured, iFind is the preferred data source. For equivalent iFind tools, see
china-market-dataskill.
Primary Sources
| Data | Source | Notes |
|---|---|---|
| 基金估值报告 | Fund administrator | Monthly/quarterly NAV |
| 投资项目数据 | Portfolio company reports | Financials, valuations |
| 交易文件 | Investment agreements | Deal terms, waterfalls |
| 清科 / 投中 | Industry data | Benchmark comparisons |
Secondary Sources
- 巨潮 — public portfolio company filings
- AkShare — market data for public holdings
- 交易所 — trading data
Workflow
Step 1: Fund-Level Summary
Fund overview table:
| Fund | Vintage | Size (亿) | Committed | Called | Distributed | NAV | TVPI | IRR |
|---|---|---|---|---|---|---|---|---|
| Fund I | 2018 | 30 | ||||||
| Fund II | 2021 | 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.
- 1mo ago First seen · 177 lines · 80 tokens per session scan A a8389bd32718
china-returns-analysis is a skill published in the GitHub repository jwangkun/claude-for-financial-services-cn (844 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,751 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 2026-09-03.
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