cn-finance-data

cn-finance-data is a skill for Claude Code from serejaris/kimi-skills. It costs 102 tokens per session (802 once invoked), scanned A, original, MIT.

A data connector for Tushare Pro, a service that provides Chinese financial and economic data. It covers stocks, funds, futures, bonds, company reports, indexes, and indicators such as GDP and CPI.

In plain words
What is it for?
Use it to retrieve market history, company financial measures, stock lists, fund values, index data, or Chinese economic indicators for analysis.
Why use it?
It removes the need to find and combine data from many separate financial sources. Results are returned as pandas tables that Python programs can use.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to retrieve market history, company financial measures, stock lists, fund values, index data, or Chinese economic indicators for analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/serejaris/kimi-skills/cn-finance-data
Install

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.

Any agent
npx skills add serejaris/kimi-skills --skill cn-finance-data
Clone the repo
git clone --depth 1 https://github.com/serejaris/kimi-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for cn-finance-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/serejaris/kimi-skills/cn-finance-data/github.svg)](https://agentmods.dev/skills/serejaris/kimi-skills/cn-finance-data)
Your own site
<a href="https://agentmods.dev/skills/serejaris/kimi-skills/cn-finance-data"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/cn-finance-data/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.

agentmods 80×15 button for cn-finance-data

Your own site · 80×15
<a href="https://agentmods.dev/skills/serejaris/kimi-skills/cn-finance-data"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/cn-finance-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 802 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00102 $0.00802
Opus 5.5 $0.00041 $0.00321
Sonnet 5.5 $0.00020 $0.00160
Haiku 4.5 $0.00010 $0.00080

Measured 1mo ago against content hash 796043ae8e98, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cn-finance-data 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.

skills/cn-finance-data/SKILL.md · 92 lines

What it actually says

Tushare 金融数据 Skill

本 skill 通过 Tushare Pro API 获取中国金融市场数据,支持 220+ 个数据接口。

快速开始

1. Token 配置

询问用户:是否已配置 Tushare Token?

如未配置,引导用户:

  1. 访问 https://tushare.pro 注册
  2. 获取 Token
  3. 配置环境变量:export TUSHARE_TOKEN="your_token"

2. 验证依赖

检查 Python 环境:

python -c "import tushare, pandas; print('OK')"

如报错,安装依赖:

pip install tushare pandas

常用接口速查

数据类型 接口方法 说明
股票列表 pro.stock_basic() 获取所有股票列表
日线行情 pro.daily() 获取日线行情数据
财务指标 pro.fina_indicator() 财务指标(ROE等)
利润表 pro.income() 利润表数据
指数行情 pro.index_daily() 指数日线数据
基金净值 pro.fund_nav() 基金净值数据
GDP数据 pro.gdp() 国内生产总值
CPI数据 pro.cpi() 居民消费价格指数

完整接口列表:查看 接口文档索引

数据获取流程

  1. 查找接口:根据需求在 接口索引 找到对应接口
  2. 阅读文档:查看 reference/接口文档/[接口名].md 了解参数
  3. 编写代码:
    import tushare as ts
    
    # 初始化(使用环境变量中的 Token)
    pro = ts.pro_api()
    
    # 调用接口
    df = pro.daily(ts_code='000001.SZ', start_date='20241201', end_date='20241231')
    
  4. 返回结果:DataFrame 格式

参数格式说明

  • 日期:YYYYMMDD(如 20241231)
  • 股票代码:ts_code 格式(如 000001.SZ, 600000.SH)
  • 返回格式:pandas DataFrame

接口文档参考

接口索引:reference/README.md

接口文档按类别组织:

  • 股票数据(39 个接口)
  • 指数数据(18 个接口)
  • 基金数据(11 个接口)
  • 期货期权(16 个接口)
  • 宏观经济(10 个接口)
  • 港股美股(23 个接口)
  • 债券数据(16 个接口)

参考资源

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 1mo ago First seen · 92 lines · 102 tokens per session scan A 796043ae8e98

Subscribe to this mod's changes

cn-finance-data is a skill published in the GitHub repository serejaris/kimi-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 102 tokens to every session and 802 once invoked, about $0.0004 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 2026-08-31.

Related

Other skills, from other repositories

ai-product-canvas

Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation…

mohitagw15856/pm-claude-skills · 73 tokens

prompt-regression-suite

Design a regression test suite that catches an LLM feature getting worse when the prompt, model, or context changes. Use when asked to stop prompt changes breaking production, set up golden tests or CI gates for an LLM feature, or test a model/prompt upgrade before shipping it. Produces a golden case set, per-case…

mohitagw15856/pm-claude-skills · 102 tokens

ai-roi-audit

Audit whether the organisation's AI spend actually paid — measured against baselines, not vendor math or vibes. Use when a CFO asks what the AI tools returned, when renewing AI contracts, when consolidating overlapping AI subscriptions, or to build the measurement plan before the next spend. Produces an ROI audit with…

mohitagw15856/pm-claude-skills · 118 tokens

credit-memo

Write a credit memo for a lending decision: borrower story, facility structure, repayment sources, financial-ratio spread with covenant headroom, risk factors with mitigants, risk-rating rationale, and a recommendation. Use when asked to write a credit memo, credit application, credit paper, loan write-up, or prepare…

mohitagw15856/pm-claude-skills · 83 tokens

loan-covenant-review

Run a quarterly loan covenant compliance review: covenant table with required vs actual vs headroom, trend and trajectory-to-breach analysis, waiver and amendment options with pricing implications, early-warning indicators, and a watch-list recommendation. Use when asked to review covenant compliance, check covenant…

mohitagw15856/pm-claude-skills · 95 tokens

ai-context-primer

Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are…

mohitagw15856/pm-claude-skills · 149 tokens