Data Report Generator — CSV/Excel to Word/PDF with Charts

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name: data-report-generator description: | Automatically analyze CSV or Excel files and generate professional data analysis reports with charts, summaries, and insights — output as Word (.docx) or PDF. Use this skill whenever the user uploads or mentions a data file (CSV, Excel, .xlsx, .xls) and wants a report, analysis, summary, dashboard, or visualization of that data. Also trigger when users say things like "analyze my data", "make a report from this spreadsheet", "visualize this CSV", "generate charts from my sales data", "weekly/monthly report", "data summary", "turn this into a report", or ask for any kind of automated reporting from tabular data. Ideal for operations, sales, marketing, and finance teams. version: 1.0.0 metadata: openclaw: emoji: "📊" requires: bins: - python3 - pip

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Data Report Generator

Transform raw CSV or Excel files into professional, insight-rich reports with charts — output as Word (.docx) or PDF.

When This Skill Activates

Trigger when user provides:

A CSV, Excel (.xlsx/.xls), or TSV file + asks for a report/analysis

A request to "visualize", "summarize", or "analyze" tabular data

Any mention of automated weekly/monthly reporting

If no file is uploaded yet, ask the user to upload their data file first.

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Step 1: Gather Inputs

Ask for (or infer from context): 1. Data file — CSV, Excel, TSV (required) 2. Report format — Word (.docx) or PDF? (default: Word) 3. Report purpose — sales analysis? operations? marketing? financial? (affects framing) 4. Key questions — what does the user most want to understand from this data? 5. Audience — internal team? executive summary? client-facing? 6. Language — Chinese or English? (default: match user's language)

If purpose/questions aren't specified, proceed with a comprehensive general analysis.

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Step 2: Read and Profile the Data

import pandas as pd
import numpy as np
Support both CSV and Excel
def load_data(filepath):
    ext = filepath.rsplit('.', 1)[-1].lower()
    if ext in ['xlsx', 'xls']:
        df = pd.read_excel(filepath)
    elif ext == 'csv':
        # Try common encodings
        for enc in ['utf-8', 'gbk', 'gb2312', 'utf-8-sig']:
            try:
                df = pd.read_csv(filepath, encoding=enc)
                break
            except:
                continue
    elif ext == 'tsv':
        df = pd.read_csv(filepath, sep='\t')
    return df
df = load_data('/path/to/file')

Data Profile to Extract

Shape: row count, column count

Column types: numeric, categorical, date/time, text

Missing values: count and % per column

Basic stats: mean, median, min, max, std for numeric columns

Unique value counts for categorical columns

Date range if time columns exist

Obvious data quality issues

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Step 3: Auto-Detect Report Type

Based on column names and data types, auto-select the analysis approach:

| Data Pattern | Report Type | Reference File | |---|---|---| | Date column + numeric values | Time Series / Trend | references/time-series.md | | Category + numeric (sales/revenue) | Sales / Performance | references/sales-analysis.md | | Multiple numeric columns | Correlation / Distribution | references/statistical.md | | Category breakdowns only | Segmentation | references/segmentation.md | | Mixed / unknown | General Analysis | references/general.md |

Read the relevant reference file for chart selection and narrative guidance.

If the user specifies a report type, use that. Otherwise, auto-detect.

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Step 4: Generate Charts

Install dependencies first:

pip install matplotlib seaborn pandas openpyxl --break-system-packages --quiet

Chart Generation Rules

import matplotlib
matplotlib.use('Agg')  # Non-interactive backend — ALWAYS set this
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import seaborn as sns
Style setup — professional look
plt.style.use('seaborn-v0_8-whitegrid')
COLORS = ['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#3B1F2B', '#44BBA4']
def save_chart(fig, filename):
    fig.savefig(filename, dpi=150, bbox_inches='tight', facecolor='white')
    plt.close(fig)

Chart Selection Guide

Time series data → Line chart (trend) + bar chart (period comparison)

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(df['date'], df['value'], color=COLORS[0], linewidth=2, marker='o', markersize=4)
ax.set_title('Trend Over Time', fontsize=14, fontweight='bold')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.xticks(rotation=45)
save_chart(fig, 'chart_trend.png')

Category comparison → Horizontal bar chart (easier to read labels)

fig, ax = plt.subplots(figsize=(10, 6))
df_sorted = df.sort_values('value', ascending=True)
bars = ax.barh(df_sorted['category'], df_sorted['value'], color=COLORS[0])
ax.bar_label(bars, fmt='%.1f', padding=3)
ax.set_title('Performance by Category', fontsize=14, fontweight='bold')
save_chart(fig, 'chart_category.png')

Distribution → Histogram + optional KDE

fig, ax = plt.subplots(figsize=(8, 5))
ax.hist(df['value'].dropna(), bins=30, color=COLORS[0], edgecolor='white', alpha=0.8)
ax.set_title('Value Distribution', fontsize=14, fontweight='bold')
save_chart(fig, 'chart_dist.png')

Composition/share → Pie or stacked bar (prefer stacked bar for >5 categories)

fig, ax = plt.subplots(figsize=(8, 6))
sizes = df['value']
labels = df['category']
wedges, texts, autotexts = ax.pie(sizes, labels=labels, autopct='%1.1f%%',
                                   colors=COLORS, startangle=90)
ax.set_title('Composition', fontsize=14, fontweight='bold')
save_chart(fig, 'chart_pie.png')

Correlation → Heatmap

fig, ax = plt.subplots(figsize=(10, 8))
corr = df[numeric_cols].corr()
sns.heatmap(corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0,
            ax=ax, square=True, cbar_kws={'shrink': 0.8})
ax.set_title('Correlation Matrix', fontsize=14, fontweight='bold')
save_chart(fig, 'chart_corr.png')

Generate 3–6 charts total — enough to be comprehensive, not overwhelming.

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Step 5: Write the Report (Word .docx)

Use the docx Python library. Install: pip install python-docx --break-system-packages --quiet

Report Structure

1. Cover Page
   - Report title, data source name, date generated
2. Executive Summary (1 page)
   - 3-5 key findings in plain language
   - Most important metric highlighted
3. Data Overview
   - Dataset dimensions, time range, data quality notes
4. [Core Analysis Sections — vary by report type]
   - Each section: narrative paragraph + chart + key takeaways
5. Key Insights & Recommendations
   - Actionable bullet points
6. Appendix: Data Statistics Table

Docx Code Pattern

from docx import Document
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
import datetime
doc = Document()
Page setup
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11)
section.left_margin = Inches(1)
section.right_margin = Inches(1)
section.top_margin = Inches(1)
section.bottom_margin = Inches(1)
Title style
def add_title(doc, text, level=1):
    heading = doc.add_heading(text, level=level)
    heading.alignment = WD_ALIGN_PARAGRAPH.LEFT
    run = heading.runs[0]
    run.font.color.rgb = RGBColor(0x2E, 0x86, 0xAB)  # Brand blue
    return heading
Body text
def add_paragraph(doc, text):
    p = doc.add_paragraph(text)
    p.paragraph_format.space_after = Pt(6)
    run = p.runs[0] if p.runs else p.add_run()
    run.font.size = Pt(11)
    return p
Insert chart image
def add_chart(doc, image_path, caption, width=6.0):
    doc.add_picture(image_path, width=Inches(width))
    last_paragraph = doc.paragraphs[-1]
    last_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER
    cap = doc.add_paragraph(caption)
    cap.alignment = WD_ALIGN_PARAGRAPH.CENTER
    cap.runs[0].font.size = Pt(9)
    cap.runs[0].font.color.rgb = RGBColor(0x66, 0x66, 0x66)
Stats table
def add_stats_table(doc, df):
    stats = df.describe().round(2)
    table = doc.add_table(rows=len(stats)+1, cols=len(stats.columns)+1)
    table.style = 'Table Grid'
    # Header row
    table.cell(0, 0).text = 'Metric'
    for j, col in enumerate(stats.columns):
        table.cell(0, j+1).text = str(col)
    # Data rows
    for i, idx in enumerate(stats.index):
        table.cell(i+1, 0).text = str(idx)
        for j, col in enumerate(stats.columns):
            table.cell(i+1, j+1).text = str(stats.loc[idx, col])
Save
doc.save('/home/claude/report_output.docx')

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Step 6: Convert to PDF (if requested)

# Use LibreOffice for conversion
python /path/to/skills/docx/scripts/office/soffice.py \
  --headless --convert-to pdf /home/claude/report_output.docx \
  --outdir /home/claude/

If LibreOffice is unavailable, fall back to reportlab:

pip install reportlab --break-system-packages --quiet

See references/pdf-fallback.md for reportlab-based PDF generation.

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Step 7: Output to User

1. Copy final file to /mnt/user-data/outputs/ 2. Use present_files tool to share it 3. Include a brief summary in chat: - What data was analyzed - How many charts were generated - Top 3 insights found - Any data quality issues to be aware of

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Output Quality Checklist

Before delivering, verify:

[ ] Charts have titles, axis labels, and readable fonts

[ ] No blank/empty chart images

[ ] Report has executive summary with plain-language findings

[ ] Data quality issues are noted (missing values, anomalies)

[ ] File opens cleanly (test docx structure validity)

[ ] Chinese text renders correctly if using Chinese (use SimHei or Microsoft YaHei font in matplotlib)

[ ] All chart image files exist before embedding in docx

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Handling Chinese / CJK Text in Charts

import matplotlib
Set Chinese-compatible font
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 
                                           'PingFang SC', 'DejaVu Sans']
matplotlib.rcParams['axes.unicode_minus'] = False  # Fix minus sign rendering

If font not available on system:

pip install matplotlib --break-system-packages
Download and install a CJK font
apt-get install -y fonts-wqy-zenhei 2>/dev/null || true

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Edge Cases

Large datasets (>100k rows): Sample or aggregate before visualization. Note sampling in report.

Messy/dirty data: Document cleaning steps in the report's "Data Overview" section. Show before/after counts.

Single column data: Generate distribution analysis only. Note limitation.

All categorical data: Focus on frequency analysis and cross-tabulations. No numeric charts.

Date parsing issues: Try multiple date formats (dayfirst=True, yearfirst=True, infer_datetime_format=True).

Multiple sheets in Excel: Ask user which sheet(s) to analyze, or analyze all and create multi-section report.

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Reference Files

references/time-series.md — Trend analysis, seasonality detection, period-over-period comparisons

references/sales-analysis.md — Sales/revenue specific charts, KPI calculations, funnel analysis

references/statistical.md — Correlation, distribution, outlier detection, regression

references/segmentation.md — Category breakdowns, cohort analysis, ranking

references/general.md — General-purpose analysis for unknown data types

references/pdf-fallback.md — PDF generation with reportlab when LibreOffice unavailable

references/chart-styling.md — Advanced chart styling, brand colors, annotation patterns