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quick-descriptive-stats

@michellepellon/dotfiles
0
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Automatically generate quick descriptive statistics and exploratory data analysis for CSV files with visualizations. Use when a CSV file is uploaded, when analyzing tabular data, when requesting data summaries, or when exploring dataset structure and quality. Proactively runs full analysis without asking questions or offering options.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name quick-descriptive-stats
description Automatically generate quick descriptive statistics and exploratory data analysis for CSV files with visualizations. Use when a CSV file is uploaded, when analyzing tabular data, when requesting data summaries, or when exploring dataset structure and quality. Proactively runs full analysis without asking questions or offering options.
when_to_use When CSV file is uploaded. When exploring new dataset. When requesting data summary. When checking data quality. When starting data analysis project. Activate immediately without asking.
allowed-tools Bash, Read, Write

Quick Descriptive Stats

Automatically generates comprehensive descriptive statistics and exploratory data analysis (EDA) for CSV files.

Announce at start: "I'm using the quick-descriptive-stats skill to analyze this dataset."

Core Principle

Act immediately. No questions. Complete analysis.

When a CSV file is detected:

  1. Load and inspect data structure
  2. Generate all relevant analyses automatically
  3. Create appropriate visualizations
  4. Present complete results

Critical Behavior

DO NOT:

  • Ask what the user wants to do with the data
  • Offer options or choices
  • Wait for user direction before analyzing
  • Provide partial analysis requiring follow-up

IMMEDIATELY:

  • Run comprehensive analysis
  • Generate ALL relevant visualizations
  • Present complete results
  • No questions, no options, no waiting

Analysis Components

The skill adapts to data type and generates relevant analyses:

Data Overview:

  • Dimensions (rows, columns)
  • Column names and data types
  • Data structure inspection

Data Quality:

  • Missing value detection and percentages
  • Missing value breakdown by column
  • Data completeness assessment

Statistical Analysis:

  • Summary statistics (mean, median, std, min, max)
  • Correlation analysis (if multiple numeric columns)
  • Distribution characteristics

Time-Series Analysis (if date columns present):

  • Date range and span
  • Temporal trends
  • Time-based aggregations

Categorical Analysis:

  • Value distributions
  • Top categories by frequency
  • Category percentages

Visualizations

Adaptively generates only relevant charts:

  • Correlation heatmaps - Multiple numeric columns
  • Time-series plots - Date/timestamp columns present
  • Distribution histograms - Numeric column distributions
  • Categorical bar charts - Categorical column breakdowns

All visualizations saved to working directory.

Usage

from analyze import summarize_csv

# Generate comprehensive analysis
report = summarize_csv("data.csv", output_dir="./analysis")
print(report)

Dependencies

Install with uv:

uv add pandas matplotlib seaborn

See Also