Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jwangkun/claude-for-financial-services-cnnpx agentmods add skills/jwangkun/claude-for-financial-services-cn/china-pptx-authorWrote 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-pptx-author)<a href="https://agentmods.dev/skills/jwangkun/claude-for-financial-services-cn/china-pptx-author"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-pptx-author/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-pptx-author"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-pptx-author.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 | $0.00145 | $0.02152 |
| Opus 5.5 | $0.00058 | $0.00861 |
| Sonnet 5.5 | $0.00029 | $0.00430 |
| Haiku 4.5 | $0.00015 | $0.00215 |
Grade A, and why
china-pptx-author 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
china-pptx-author
Purpose
Generate professional A股投资分析PPT for any listed company. This skill is a generic engine — every output is driven by two parameters:
| Parameter | Example | Description |
|---|---|---|
{{COMPANY_NAME}} |
{{COMPANY_NAME}} | Full Chinese company name (e.g., 贵州茅台) |
{{TICKER}} |
{{TICKER}} | 6-digit A-share code (e.g., 600519) |
{{OUTPUT_PATH}} |
./output.pptx | Where to save the PPTX file |
All financial figures, price data, peer valuations, and company descriptions are fetched live from iFind / AkShare / public APIs. Nothing is hardcoded.
Data Pipeline
Step 1: Resolve Company Info
# MCP tool: search_stock
search_stock(keyword="{{COMPANY_NAME}}")
# → confirms ticker, exchange (SH/SZ), full legal name
# MCP tool: get_quote
get_quote(ticker="{{TICKER}}")
# → live price, PE, PB, market cap, turnover, 52-week range
# MCP tool: get_financials
get_financials(ticker="{{TICKER}}", statement_type="income", period="annual")
# → annual income statement: revenue, net profit, margins, EPS, etc.
get_financials(ticker="{{TICKER}}", statement_type="balance", period="annual")
# → balance sheet: assets, liabilities, equity
get_financials(ticker="{{TICKER}}", statement_type="cashflow", period="annual")
# → cash flow statement
# MCP tool: get_historical_data
get_historical_data(ticker="{{TICKER}}", frequency="weekly", start_date="{{START_DATE}}", end_date="{{END_DATE}}")
# → OHLCV price history for trend charts
# MCP tool: get_industry_stocks
get_industry_stocks(industry="{{INDUSTRY_NAME}}")
# → peer companies in the same sector for comps analysis
Step 2: Fetch Peer Data
# For each peer returned by get_industry_stocks, call get_quote to get PE/PB
# Focus on top 5-8 comparable companies by revenue/market cap
Step 3: Generate Charts
Use matplotlib to create:
- Revenue & Profit trend — from
get_financials(income) - Margin trends — gross margin, net margin, ROE over time
- Growth rates — YoY revenue and profit growth
- Peer comparison — horizontal bar chart of PE and PB vs peers
- Price trend — from
get_historical_data - Valuation range — football field based on peer PE distribution
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 · 241 lines · 145 tokens per session scan A 160fb84b2ac7
china-pptx-author 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 145 tokens to every session and 2,152 once invoked, about $0.0006 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.
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