pareto-analyzer
Pareto analysis skill for identifying vital few causes and prioritizing improvement efforts.
Profile →npx skills add a5c-ai/babysitter --skill pareto-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# pareto-analyzer You are **pareto-analyzer** - a specialized skill for Pareto analysis to identify the vital few causes and prioritize improvement efforts. ## Overview This skill enables AI-powered Pareto analysis including: - Basic Pareto chart creation - Multi-level Pareto analysis - Weighted Pareto analysis - Before/after comparison - Pareto by multiple dimensions - Statistical validation - Vital few identification - Improvement prioritization ## Capabilities ### 1. Basic Pareto Analysis ```python import pandas as pd import numpy as np def pareto_analysis(data: pd.DataFrame, category_col: str, value_col: str): """ Perform basic Pareto analysis data: DataFrame with categories and values category_col: column name for categories value_col: column name for values (counts, costs, etc.) """ # Aggregate by category summary = data.groupby(category_col)[value_col].sum().reset_index() summary.columns = ['category', 'value'] # Sort descending summary = summary.sort_values('value', ascending=False).reset_index(drop=True) # Calculate percentages total = summary['value'].sum() summary['percentage'] = summary['value'] / total * 100 summary['cumulative_value'] = summary['value'].cumsum() summa
- Overview
- Capabilities
- 1. Basic Pareto Analysis
- 2. Multi-Level Pareto
- 3. Weighted Pareto Analysis
- 4. Before/After Pareto Comparison
- 5. Pareto Chart Data Generation
- 6. Statistical Validation
- Process Integration
- Output Format
- Best Practices
- Constraints
What does the pareto-analyzer skill do?
Pareto analysis skill for identifying vital few causes and prioritizing improvement efforts.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill pareto-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.