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pareto-analysis

Apply Pareto analysis to datasets and generate SVG charts and HTML reports.

10|5|Updated Jan 2, 2026
Search Tags:#analysis#prioritization#root-cause#data-visualization#pareto#quality-improvement
One-click install
npx skills add https://github.com/ddunnock/claude-plugins --skill pareto-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pareto-analysis
Source: https://github.com/ddunnock/claude-plugins/tree/main/skills/pareto-analysis
Command: npx skills add https://github.com/ddunnock/claude-plugins --skill pareto-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Pareto Analysis helps teams quickly pinpoint the few causes that drive the majority of problems, enabling focused improvements and efficient allocation of resources.

Core Features & Use Cases

  • 5-Phase workflow: Problem scoping, data collection, chart construction, analysis, and documentation.
  • Visual outputs: SVG Pareto charts and HTML reports that highlight the vital few.
  • Flexible data handling: Works with frequency, cost, time, or weighted measures; provides MECE guidance and data-quality checks.
  • Use Case: Prioritize defects in manufacturing or IT service issues by identifying top contributing categories and focusing corrective actions.

Quick Start

Prepare a dataset with categories and numeric values, then run the calculation and generate outputs.

  • Create a JSON file data.json with structure: { "data": [ {"category": "A", "value": 45}, ... ] }
  • Run: python3 scripts/calculate_pareto.py --input data.json
  • Generate chart: python3 scripts/generate_chart.py --input data.json --output pareto_chart.svg
  • Produce report: python3 scripts/generate_report.py --input data.json --output pareto_report.html

Frequently Asked Questions about pareto-analysis

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

FAQPage Schema
How do I identify the vital few causes driving most of my defects or service issues?▼

Pareto analysis identifies the vital few causes driving most defects by sorting categories by value, computing cumulative percentages, and flagging top contributors. This pinpoints where focused improvement efforts yield the greatest impact across manufacturing, IT, and service contexts.

What is the best way to generate a Pareto chart and HTML report from raw category data?▼

You generate a Pareto chart and HTML report by preparing a JSON file with category and value pairs, then running Python scripts to calculate cumulative shares, produce an SVG chart, and output an HTML report highlighting the vital few categories.

Can I use Pareto analysis with weighted measures like cost and time instead of just frequency counts?▼

Yes, Pareto analysis works with frequency, cost, time, or weighted measures. It provides MECE categorization guidance and data-quality checks to ensure accurate prioritization regardless of the numeric measure applied to your dataset.

Does this Pareto analysis approach support data visualization outputs for quality improvement reporting?▼

Yes, the approach supports quality improvement reporting by producing visual SVG Pareto charts and HTML reports. These outputs highlight the vital few categories, enabling teams to visualize problem distribution and document corrective actions efficiently.

What steps are involved in applying Pareto analysis for root-cause prioritization?▼

Applying Pareto analysis for root-cause prioritization involves a 5-phase workflow: scoping the problem, collecting data, constructing the chart, analyzing the vital few, and documenting results. This structured process ensures accurate data categorization and targeted improvement efforts.

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