report-generator

Q: How do I install report-generator?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill report-generator in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/report-generator/ (personal) or .claude/skills/report-generator/ (project).

by dkyazzentwatwaDocs & Writing

Generate professional PDF/HTML reports with charts, tables, and narrative from data. Supports templates, branding, and automated report generation.

Installs: 153
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill report-generator

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads report-generator automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Report Generator

Create professional, data-driven reports with charts, tables, and narrative text. Perfect for business reports, analytics dashboards, status updates, and automated reporting pipelines.

Quick Start

from scripts.report_gen import ReportGenerator

# Create a simple report
report = ReportGenerator("Monthly Sales Report")
report.add_text("This report summarizes sales performance for Q4 2024.")
report.add_table(sales_data, title="Sales by Region")
report.add_chart(sales_data, chart_type="bar", title="Revenue by Month")
report.add_text("Key findings: Revenue increased 25% quarter-over-quarter.")
report.generate().save("sales_report.pdf")

# From template
report = ReportGenerator.from_template("executive_summary")
report.set_data(data_dict)
report.generate().save("exec_summary.pdf")

Features

Multiple Output Formats: PDF, HTML
Rich Content: Text, tables, charts, images, headers
Chart Types: Bar, line, pie, scatter, area, heatmap
Table Formatting: Auto-styling, conditional formatting
Templates: Pre-built report templates
Branding: Logo, colors, fonts, headers/footers
Sections: Table of contents, page numbers, appendices
Data Integration: CSV, DataFrame, dict inputs

API Reference

Initialization

# New report
report = ReportGenerator("Report Title")
report = ReportGenerator("Report Title", subtitle="Q4 2024 Analysis")

# From template
report = ReportGenerator.from_template("quarterly_review")

# With config
report = ReportGenerator("Title", config={
    "page_size": "letter",
    "orientation": "portrait",
    "margins": {"top": 1, "bottom": 1, "left": 0.75, "right": 0.75}
})

Report Metadata

# Title and subtitle
report.set_title("Annual Report 2024")
report.set_subtitle("Financial Performance Analysis")

# Author and date
report.set_author("Analytics Team")
report.set_date("December 2024")
report.set_date_auto()  # Use today

# Organization
report.set_organization("Acme Corporation")
report.set_logo("logo.png")

Adding Content

Text Content

# Simple paragraph
report.add_text("This is a paragraph of analysis text.")

# Styled text
report.add_text("Important finding!", style="highlight")
report.add_text("Key metric: 42%", style="metric")

# Headers
report.add_heading("Executive Summary", level=1)
report.add_heading("Revenue Analysis", level=2)
report.add_heading("By Region", level=3)

# Bullet points
report.add_bullets([
    "Revenue increased 25% YoY",
    "Customer acquisition up 15%",
    "Churn rate decreased to 3%"
])

# Numbered list
report.add_numbered_list([
    "Expand to European markets",
    "Launch mobile application",
    "Implement AI-driven analytics"
])

Tables

# From DataFrame
import pandas as pd
df = pd.DataFrame({
    'Region': ['North', 'South', 'East', 'West'],
    'Revenue': [100000, 85000, 92000, 78000],
    'Growth': ['12%', '8%', '15%', '5%']
})
report.add_table(df, title="Regional Performance")

# From dict/list
data = [
    {'Product': 'A', 'Sales': 1000, 'Profit': 200},
    {'Product': 'B', 'Sales': 1500, 'Profit': 350}
]
report.add_table(data, title="Product Summary")

# With styling
report.add_table(df, title="Sales Data",
    highlight_max=['Revenue'],       # Highlight max values
    highlight_min=['Growth'],        # Highlight min values
    currency_cols=['Revenue'],       # Format as currency
    percent_cols=['Growth'],         # Format as percent
    align={'Region': 'left', 'Revenue': 'right'}
)

Charts

# Bar chart
report.add_chart(
    data=df,
    chart_type="bar",
    x="Region",
    y="Revenue",
    title="Revenue by Region"
)

# Line chart
report.add_chart(
    data=time_series_df,
    chart_type="line",
    x="Month",
    y=["Sales", "Forecast"],
    title="Sales Trend"
)

# Pie chart
report.add_chart(
    data=category_df,
    chart_type="pie",
    values="Amount",
    labels="Category",
    title="Budget Allocation"
)

# Chart options
report.add_chart(
    data=df,
    chart_type="bar",
    x="Region",
    y="Revenue",
    title="Revenue Analysis",
    color="#3498db",
    width=6,           # inches
    height=4,
    show_values=True,
    show_legend=True
)

Images

# Add image
report.add_image("screenshot.png", caption="Dashboard View")
report.add_image("diagram.png", width=5, caption="Architecture Diagram")

Special Elements

# Page break
report.add_page_break()

# Horizontal line
report.add_divider()

# Spacer
report.add_spacer(height=0.5)  # inches

# Callout box
report.add_callout(
    "Key Insight: Customer retention improved 20% after implementing the new onboarding flow.",
    style="info"  # info, warning, success, error
)

# Quote
report.add_quote(
    "Data is the new oil.",
    attribution="Clive Humby"
)

Sections and Structure

# Start a new section
report.start_section("Financial Analysis")

# Add content to section
report.add_text("...")
report.add_table(...)

# End section
report.end_section()

# Table of contents (auto-generated)
report.enable_toc()

# Appendix
report.start_appendix()
report.add_heading("Raw Data", level=2)
report.add_table(raw_data)

Branding and Styling

# Logo and organization
report.set_logo("logo.png", width=150)
report.set_organization("Acme Corp")

# Colors
report.set_colors(
    primary="#1e40af",      # Headers, accents
    secondary="#6b7280",    # Secondary text
    background="#ffffff"    # Background
)

# Fonts
report.set_fonts(
    heading="Helvetica-Bold",
    body="Helvetica"
)

# Header and footer
report.set_header("Confidential - Internal Use Only")
report.set_footer("Page {page} of {total}")

# Watermark
report.set_watermark("DRAFT")

Templates

# Available templates
report = ReportGenerator.from_template("executive_summary")
report = ReportGenerator.from_template("quarterly_review")
report = ReportGenerator.from_template("project_status")
report = ReportGenerator.from_template("analytics_dashboard")

# Template with data
report = ReportGenerator.from_template("monthly_metrics")
report.set_data({
    "period": "December 2024",
    "revenue": 1500000,
    "growth": 0.15,
    "customers": 5000,
    "charts": {"revenue_trend": trend_df}
})
report.generate()

Generation and Export

# Generate report
report.generate()

# Save as PDF
report.save("report.pdf")

# Save as HTML
report.save("report.html")

# Get bytes
pdf_bytes = report.to_bytes()
html_string = report.to_html()

Templates

Executive Summary

Title page
Key metrics highlights
Summary bullets
Charts section
Recommendations

Quarterly Review

Performance overview
Financial metrics
Comparison to previous quarter
Goals progress
Next quarter outlook

Project Status

Project overview
Timeline/milestones
Risks and issues
Team updates
Next steps

Analytics Dashboard

KPI cards
Multiple charts
Trend analysis
Data tables
Insights

CLI Usage

# Generate from JSON config
python report_gen.py --config report_config.json --output report.pdf

# With template
python report_gen.py --template executive_summary --data data.json --output summary.pdf

# Quick report from CSV
python report_gen.py --csv data.csv --title "Data Report" --output report.pdf

CLI Arguments

Argument	Description	Default
`--config`	Report configuration JSON	-
`--template`	Template name	-
`--data`	Data JSON file	-
`--csv`	CSV data file	-
`--title`	Report title	`Report`
`--output`	Output file path	`report.pdf`
`--format`	Output format (pdf/html)	`pdf`

Examples

Sales Report

report = ReportGenerator("Q4 Sales Report")
report.set_subtitle("October - December 2024")
report.set_organization("Sales Department")
report.set_logo("company_logo.png")

report.add_heading("Executive Summary", level=1)
report.add_text(
    "Q4 2024 showed strong performance across all regions, "
    "with total revenue reaching $4.2M, a 23% increase over Q3."
)

report.add_callout(
    "Total Revenue: $4.2M (+23% QoQ)",
    style="success"
)

report.add_heading("Regional Performance", level=2)
report.add_chart(regional_data, "bar", x="Region", y="Revenue",
                 title="Revenue by Region")
report.add_table(regional_data, title="Detailed Metrics")

report.add_heading("Trends", level=2)
report.add_chart(monthly_data, "line", x="Month", y="Revenue",
                 title="Monthly Revenue Trend")

report.add_heading("Recommendations", level=1)
report.add_bullets([
    "Increase investment in high-growth East region",
    "Address declining West region performance",
    "Launch Q1 promotional campaign"
])

report.generate().save("q4_sales_report.pdf")

Analytics Dashboard

report = ReportGenerator("Marketing Analytics")
report.set_date_auto()

# KPI Summary
report.add_heading("Key Metrics", level=1)
kpis = [
    ["Visitors", "125,000", "+15%"],
    ["Conversions", "3,750", "+22%"],
    ["Revenue", "$187,500", "+18%"],
    ["CAC", "$45", "-8%"]
]
report.add_table(kpis, headers=["Metric", "Value", "Change"])

# Traffic Sources
report.add_heading("Traffic Sources", level=2)
report.add_chart(traffic_df, "pie", values="Sessions", labels="Source",
                 title="Traffic Distribution")

# Conversion Funnel
report.add_heading("Conversion Funnel", level=2)
report.add_chart(funnel_df, "bar", x="Stage", y="Users",
                 title="Funnel Analysis", horizontal=True)

# Trend Analysis
report.add_heading("Trends", level=2)
report.add_chart(daily_df, "line", x="Date", y=["Visitors", "Conversions"],
                 title="Daily Performance")

report.generate().save("marketing_dashboard.pdf")

Dependencies

reportlab>=4.0.0
Pillow>=10.0.0
pandas>=2.0.0
matplotlib>=3.7.0

Limitations

Charts rendered as static images in PDF
Complex layouts may need manual adjustment
Large datasets may impact performance
HTML output has basic styling (no interactive charts)

FAQ

What does the report-generator skill do?

Generate professional PDF/HTML reports with charts, tables, and narrative from data. Supports templates, branding, and automated report generation.

How do I install report-generator?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill report-generator in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/report-generator/ (personal) or .claude/skills/report-generator/ (project).

Which AI agents can use report-generator?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is report-generator safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains report-generator?

It is published by dkyazzentwatwa in dkyazzentwatwa/chatgpt-skills. It has 153 recorded installs.

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report-generator

Install

Install

Describe the task

Review the result

SKILL.md

Report Generator

Quick Start

Features

API Reference

Initialization

Report Metadata

Adding Content

Text Content

Tables

Charts

Images

Special Elements

Sections and Structure

Branding and Styling

Templates

Generation and Export

Templates

Executive Summary

Quarterly Review

Project Status

Analytics Dashboard

CLI Usage

CLI Arguments

Examples

Sales Report

Analytics Dashboard

Dependencies

Limitations

FAQ

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