Value-investing analysis tool for Chinese A-shares with stock screening, deep financial analysis, and valuation modeling.
Works with
Four core modules: stock screener (filter by PE, ROE, dividend yield, debt ratio), financial analyzer (profitability, growth, cash flow trends), industry comparator (peer benchmarking), and valuation calculator (DCF, DDM, relative valuation methods)
Automated financial anomaly detection flags suspicious patterns in receivables, cash flow divergence, inventory buildup
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionchina-stock-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches china-stock-analysis from sugarforever/01coder-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate china-stock-analysis. Access via /china-stock-analysis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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total installs
2
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Run in your terminal
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基于价值投资理论的中国A股分析工具，面向低频交易的普通投资者。
当用户请求以下操作时调用此skill：
pip install akshare pandas numpy
在执行任何分析前，先检查akshare是否已安装：
python -c "import akshare; print(akshare.__version__)"
如果未安装，提示用户安装：
pip install akshare
筛选符合条件的股票
个股深度财务分析
同行业横向对比分析
内在价值测算与安全边际计算
用户请求筛选股票时使用。
向用户询问筛选条件。提供以下选项供用户选择或自定义：
估值指标：
盈利能力：
成长性：
股息：
财务安全：
筛选范围：
python scripts/stock_screener.py \
--scope "hs300" \
--pe-max 15 \
--roe-min 15 \
--debt-ratio-max 60 \
--dividend-min 2 \
--output screening_result.json
参数说明：
--scope: 筛选范围 (all/hs300/zz500/cyb/kcb/custom:600519,000858,...)--pe-max/--pe-min: PE范围--pb-max/--pb-min: PB范围--roe-min: 最低ROE--growth-min: 最低增长率--debt-ratio-max: 最大资产负债率--dividend-min: 最低股息率--output: 输出文件路径读取 screening_result.json 并以表格形式呈现给用户：
| 代码 | 名称 | PE | PB | ROE | 股息率 | 评分 |
|---|---|---|---|---|---|---|
| 600519 | 贵州茅台 | 25.3 | 8.5 | 30.2% | 2.1% | 85 |
用户请求分析某只股票时使用。
询问用户：
python scripts/data_fetcher.py \
--code "600519" \
--data-type all \
--years 5 \
--output stock_data.json
参数说明：
--code: 股票代码--data-type: 数据类型 (basic/financial/valuation/holder/all)--years: 获取多少年的历史数据--output: 输出文件python scripts/financial_analyzer.py \
--input stock_data.json \
--level standard \
--output analysis_result.json
参数说明：
--input: 输入的股票数据文件--level: 分析深度 (summary/standard/deep)--output: 输出文件python scripts/valuation_calculator.py \
--input stock_data.json \
--methods dcf,ddm,relative \
--discount-rate 10 \
--growth-rate 8 \
--output valuation_result.json
参数说明：
--input: 股票数据文件--methods: 估值方法 (dcf/ddm/relative/all)--discount-rate: 折现率(%)--growth-rate: 永续增长率(%)--margin-of-safety: 安全边际(%)--output: 输出文件读取分析结果，参考 templates/analysis_report.md 模板生成中文分析报告。
报告结构（标准级）：
询问用户：
python scripts/data_fetcher.py \
--codes "600519,000858,002304" \
--data-type comparison \
--output industry_data.json
或按行业获取：
python scripts/data_fetcher.py \
--industry "白酒" \
--top 10 \
--output industry_data.json
python scripts/financial_analyzer.py \
--input industry_data.json \
--mode comparison \
--output comparison_result.json
| 指标 | 贵州茅台 | 五粮液 | 洋河股份 | 行业均值 |
|---|---|---|---|---|
| PE | 25.3 | 18.2 | 15.6 | 22.4 |
| ROE | 30.2% | 22.5% | 20.1% | 18.5% |
| 毛利率 | 91.5% | 75.2% | 72.3% | 65.4% |
| 评分 | 85 | 78 | 75 | - |
询问用户估值参数（或使用默认值）：
DCF模型参数：
DDM模型参数：
相对估值参数：
python scripts/valuation_calculator.py \
--code "600519" \
--methods all \
--discount-rate 10 \
--terminal-growth 3 \
--forecast-years 5 \
--margin-of-safety 30 \
--output valuation.json
| 估值方法 | 内在价值 | 当前价格 | 安全边际价格 | 结论 |
|---|---|---|---|---|
| DCF | ¥2,150 | ¥1,680 | ¥1,505 | 低估 |
| DDM | ¥1,980 | ¥1,680 | ¥1,386 | 低估 |
| 相对估值 | ¥1,850 | ¥1,680 | ¥1,295 | 合理 |
在分析过程中自动检测以下异常信号：
应收账款异常
现金流背离
存货异常
毛利率异常
关联交易
股东减持
根据行业分类提供政策相关提示：
所有脚本输出JSON格式，便于后续处理：
{
"code": "600519",
"name": "贵州茅台",
"analysis_date": "2025-01-25",
"level": "standard",
"summary": {
"score": 85,
"conclusion": "低估",
"recommendation": "建议关注"
},
"financials": { ... },
"valuation": { ... },
"risks": [ ... ]
}
生成结构化的中文Markdown报告，参考 templates/analysis_report.md。
如果akshare数据获取失败，提示用户：
提示用户检查股票代码是否正确，提供可能的匹配建议。
对于新上市股票或财务数据不完整的情况，说明数据限制并基于可用数据进行分析。
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
china-stock-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
china-stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in china-stock-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for china-stock-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
china-stock-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: china-stock-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
china-stock-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend china-stock-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
china-stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
china-stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
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