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-ai-readinessgit 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-ai-readiness)<a href="https://agentmods.dev/skills/jwangkun/claude-for-financial-services-cn/china-ai-readiness"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-ai-readiness/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-ai-readiness"><img src="https://agentmods.dev/badge/skills/jwangkun/claude-for-financial-services-cn/china-ai-readiness.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 | $0.00079 | $0.01659 |
| Opus 5 | $0.00039 | $0.00830 |
| Sonnet 5 | $0.00016 | $0.00332 |
| Haiku 4.5 | $0.00008 | $0.00166 |
Grade A, and why
china-ai-readiness 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 9d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
china-ai-readiness
Purpose
Evaluate A股被投企业AI就绪度 — assessing portfolio companies' preparedness for AI adoption and transformation in the Chinese market context.
Data Sources
Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)
get_quote(ticker) → Company valuation context
get_financials(ticker, "income") → Revenue scale, R&D spend
get_stock_info(ticker) → Business description
Secondary Sources
- 巨潮 — company filings, R&D disclosure
- 券商研报 — technology assessments
- 行业报告 — AI adoption benchmarks
Workflow
Step 1: Assess Data Infrastructure
Data readiness dimensions:
| Dimension | Assessment | China Context |
|---|---|---|
| 数据积累 (Data accumulation) | Years of data, volume | Chinese companies often have rich transaction data |
| 数据质量 (Data quality) | Completeness, accuracy | Legacy systems may have gaps |
| 数据打通 (Data integration) | Siloed vs unified | Common challenge: ERP/WMS/CRM not integrated |
| 数据治理 (Data governance) | Policies, standards | Often underdeveloped |
| 数字化基础 (Digital foundation) | ERP, cloud adoption | Varies widely by industry/company age |
Step 2: Evaluate Technology Stack
Technology assessment:
| Layer | Questions | Typical China Status |
|---|---|---|
| 基础设施 | Cloud? On-premise? | Mix of on-premise and hybrid |
| 数据平台 | Data warehouse? BI tools? | Often Excel-heavy |
| 应用系统 | ERP, CRM, WMS, MES? | ERP common (用友, 金蝶, SAP) |
| 开发能力 | Internal IT team? | Varies; often outsourced |
| 技术投入 | IT spend as % revenue? | Typically 1-3% |
Step 3: Talent Assessment
AI/tech talent:
| Role | Availability in China | Typical Company Status |
|---|---|---|
| 数据科学家 | Scarce, expensive | Usually not in-house |
| 算法工程师 | Scarce | Outsourced or absent |
| 数据工程师 | Available | Often basic level |
| 业务分析师 | Available | Excel-based mostly |
| 数字化领导 | Rare | Gap at leadership level |
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.
- 9d ago First seen · 185 lines · 79 tokens per session scan A a3f46bea94c4
china-ai-readiness 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 79 tokens to every session and 1,659 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 2026-09-03.
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