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-variance-commentarygit 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-variance-commentary)<a href="https://agentmods.dev/skills/jwangkun/claude-for-financial-services-cn/china-variance-commentary"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-variance-commentary/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-variance-commentary"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-variance-commentary.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 | $0.00076 | $0.01464 |
| Opus 5 | $0.00038 | $0.00732 |
| Sonnet 5 | $0.00015 | $0.00293 |
| Haiku 4.5 | $0.00008 | $0.00146 |
Grade A, and why
china-variance-commentary 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
china-variance-commentary
Purpose
Write professional 基金业绩点评 — structured variance commentary on fund performance and portfolio changes.
Data Sources
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 — 同花顺 iFind（付费精确数据）/ AkShare MCP（Tier-2 免费备选）
get_fund_data(fund_code) → Fund NAV, performance
get_quote(ticker) → Individual security performance
get_index_data("000001") → Benchmark data
Secondary Sources
- 基金公司 — fund performance data
- 托管行 — custody data
- Wind / Choice — performance analytics
Workflow
Step 1: Gather Performance Data
Performance snapshot:
| Metric | Period | Benchmark | Active Return |
|---|---|---|---|
| 净值增长率 | X.XX% | X.XX% | X.XX% |
| 年化收益率 | X.XX% | X.XX% | X.XX% |
| 波动率 | X.XX% | X.XX% | |
| 夏普比率 | X.XX | ||
| 最大回撤 | X.XX% | ||
| 卡尔马比率 | X.XX |
Step 2: Attribution Analysis
Performance attribution:
| Factor | Contribution | Description |
|---|---|---|
| 资产配置 (Allocation) | X.XX% | Sector/security weight decisions |
| 个股选择 (Selection) | X.XX% | Security picking within sectors |
| 交互效应 (Interaction) | X.XX% | Combined effect |
Step 3: Sector Attribution
Sector performance:
| Sector | Weight | Return | Contribution | Benchmark Weight | Benchmark Return | Allocation Effect | Selection Effect |
|---|---|---|---|---|---|---|---|
| Total | 100% |
Step 4: Top/Bottom Contributors
Best/worst performers:
| # | Security | Ticker | Weight | Period Return | Contribution |
|---|---|---|---|---|---|
| 1 (Best) | |||||
| 2 | |||||
| 3 | |||||
| ... | |||||
| (Worst) | |||||
| (2nd Worst) | |||||
| (3rd Worst) |
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 · 191 lines · 76 tokens per session scan A 5f9f8480cd7d
china-variance-commentary 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 76 tokens to every session and 1,464 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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