Intelligent data analysis report generator

Intelligent data analysis report generator. Auto-identifies Excel/CSV data structure (dimensions, metrics, timelines), performs multi-dimensional parallel analysis, and generates professional HTML reports with ECharts interactive charts.

Data Analysis Report Generator

Generate professional HTML analysis reports from Excel/CSV data with interactive ECharts charts. Supports 11 professional report styles: FT, McKinsey, Economist, Bloomberg, HBR, Nature, Wired, NYT, WSJ, MIT Technology Review, 36Kr.

Use Cases

• User sends an Excel/CSV file for analysis
• User asks to generate a data analysis report
• User mentions keywords like '数据分析报告', '分析报告', '数据报告', 'Excel分析', 'CSV分析', 'ECharts报告
• User wants professional chart-based data visualization from tabular data

Quick Start

When user sends an Excel/CSV file:

1. Run analyzer.py on the file → get analysis.json
2. Run chart_generator.py with analysis + style → get charts.json
3. Build HTML report from template + style variables + chart data
4. Save and deliver the HTML file

Workflow

Step 1: Analyze Data

bash

$env:PYTHONIOENCODING='utf-8'; python <skill_dir>/scripts/analyzer.py <input_file> --output <workspace>/_tmp_analysis.json

Read the output JSON. Key fields:

• meta: row/column counts, structure overview
• columns: per-column type detection (dimension/metric/timeline)
• stats: descriptive statistics for metrics
• correlations: inter-metric correlations
• timeline: time-series grouping (if timeline column detected)
• top_n: top categories by metric

Step 2: Select Report Style

Default: ft. Select based on context or user preference:

Style	Key	Best For
FT	ft	Financial data, investment analysis
McKinsey	mckinsey	Business strategy, consulting
Economist	economist	Macroeconomic, policy
Bloomberg	bloomberg	Market data, terminal-style
HBR	hbr	Academic business research
Nature	nature	Scientific research
Wired	wired	Tech industry, bold visuals
NYT	nyt	General news-style
WSJ	wsj	Business/market reporting
MIT Tech Review	mit	Technology research
36Kr	36kr	Startup/tech ecosystem (Chinese)

For detailed style definitions → read references/report_styles.md.

Step 3: Generate Chart Configs

bash

$env:PYTHONIOENCODING='utf-8'; python <skill_dir>/scripts/chart_generator.py --input <workspace>/_tmp_analysis.json --style <style_key> --output <workspace>/_tmp_charts.json

Step 4: Build HTML Report

1. Read assets/report_template.html
2. Read references/style_variables.json → pick the selected style's CSS variables
3. Replace template placeholders:
   • {{REPORT_TITLE}} → descriptive report title (inferred from data)
   • {{REPORT_SUBTITLE}} → brief data summary line
   • {{REPORT_DATE}} → current date (YYYY-MM-DD)
   • {{ROW_COUNT}} → total rows
   • {{COL_COUNT}} → total columns
   • {{STYLE_LABEL}} → style display name
   • {{BG_PRIMARY}} through {{FONT_BODY}} → style CSS variables
   • {{CHART_DATA_JSON}} → charts JSON (from Step 3, only the charts object)
   • {{SUMMARY_STATS_JSON}} → stats JSON (from Step 1, the stats object)
   • {{INSIGHTS}} → AI-generated insight paragraphs (see Step 5)
4. Write the final HTML to workspace

Step 5: Generate AI Insights

Based on the analysis results, write 3–6 insight paragraphs as <p class="insight"> elements:

• Top findings: Which dimension categories dominate? Any surprising leaders?
• Trend analysis: If timeline data exists, what trends are visible? Growth/decline?
• Correlation insights: Which metrics are strongly correlated? Any unexpected relationships?
• Distribution notes: High variance metrics, outliers, or skewed distributions
• Anomalies: Missing data, extreme values, or unusual patterns

Each insight should be 1–3 sentences with specific numbers. Format:

html

<p class="insight">🔍 <strong>Finding:</strong> Category X accounts for 42% of total revenue (¥1.2M), 3.5× the average.</p>

Step 6: Deliver

Save the final HTML file to workspace and provide it to the user. The report is a self-contained HTML file that can be opened in any browser.

Multi-File Analysis

When user sends multiple Excel/CSV files:

1. Analyze each file separately
2. Generate a combined report with separate sections per file
3. Add a cross-file comparison section if files share similar structure

Customization

User Requests a Different Style

Re-run Step 3–4 with the new style key. No need to re-analyze data.

User Wants Specific Charts

After Step 1, manually construct ECharts option objects for the requested chart types. Reference references/chart_types.md for type selection rules.

User Wants Additional Analysis

Run custom pandas analysis scripts using the same data file. Add results as additional sections in the HTML report.

Dependencies

• Python 3.8+ with: pandas, openpyxl, numpy
• ECharts 5.5.0 (loaded via CDN in the HTML template — no local install needed)

If dependencies missing, install: pip install pandas openpyxl numpy

File Structure

text

data-analysis-report/
├── SKILL.md                          # This file
├── scripts/
│   ├── analyzer.py                   # Data analysis engine
│   └── chart_generator.py            # ECharts config generator
├── references/
│   ├── report_styles.md              # 11 style definitions
│   ├── chart_types.md                # Chart type selection guide
│   └── style_variables.json          # CSS variables per style
└── assets/
    └── report_template.html          # HTML report template