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carbon.report.gen

Generate structured carbon reports from Carbon ACX datasets or CSVs.

Updated Aug 9, 2025
Search Tags:#automation#data visualization#compliance#reporting#jinja2#sustainability#carbon report
One-click install
npx skills add https://github.com/chrislyons/carbon-acx --skill carbon-report-gen
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: carbon.report.gen
Source: https://github.com/chrislyons/carbon-acx/tree/main/.claude/skills/project/carbon-report-gen
Command: npx skills add https://github.com/chrislyons/carbon-acx --skill carbon-report-gen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, plotly, jinja2, Pandoc, and includes references (resource) and assets (resource) components.

What problem does it solve?

Generating consistent, professional carbon accounting reports (monthly, quarterly, compliance) is a repetitive and time-consuming task, often involving manual data aggregation and formatting. This skill automates the entire process, from data retrieval to chart generation and template population.

Core Features & Use Cases

  • Automated Report Generation: Creates structured Markdown reports (with optional PDF export) for various needs.
  • Data-Driven Visualizations: Automatically generates charts (bar, pie, line) using your carbon data.
  • Template-Based Output: Populates predefined templates, ensuring brand consistency and required sections.
  • Use Case: A finance director needs a quarterly compliance report for Q1 2024. Instead of spending days compiling data and formatting, they ask, "Create a quarterly compliance report for Q1 2024," and receive a complete, audit-ready report in minutes, so "AI works, you rest."

Quick Start

Example: Monthly Report Generation

User: "Generate a monthly carbon report for March 2025"

Output File Structure:

# Monthly Carbon Report — March 2025

Report Generated: 2025-10-18

Data Version: v1.2

Reporting Period: March 2025

## Executive Summary

This report covers carbon accounting data for March 2025...

[Key findings bullet points]

Frequently Asked Questions about carbon.report.gen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate carbon report generation from monthly to compliance reporting?▼

Automate carbon report generation by querying Carbon ACX datasets or CSVs, loading Jinja2 templates, and using pandas and matplotlib or Plotly to generate structured Markdown reports with charts and narrative analysis. Output saves to a designated reports directory with data units, sources, and provenance included.

Can I generate carbon reports with charts and export to PDF?▼

Yes, carbon reports generate as Markdown with embedded charts using matplotlib or Plotly, then optionally convert to PDF via Pandoc. This ensures audit-ready output with data visualization for compliance and executive reporting.

What data format do I need to create carbon reports?▼

Prepare carbon data as CSV files or query from Carbon ACX datasets. Data must include units, sources, and provenance; pandas processes and aggregates this data before Jinja2 templates populate the report structure and generate visualizations.

Does carbon report automation work for quarterly compliance reports?▼

Yes, the skill automates quarterly, monthly, and compliance reporting. Template-based generation ensures consistent structure, required sections, and brand alignment for audit-ready compliance reports in minutes instead of days.

What templates and components do I need to set up for report generation?▼

Set up Jinja2 report templates and asset files stored in the skill's references and assets components. Templates define report structure, sections, and formatting; the skill populates them with processed data and generated charts.

Why should I use template-based automation instead of manual report compilation?▼

Template-based automation eliminates manual data aggregation, formatting inconsistencies, and repetitive work. It ensures compliance-ready reports with standardized sections, proper data provenance, and professional visualizations in a fraction of the time.

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