| 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:
- Load and inspect data structure
- Generate all relevant analyses automatically
- Create appropriate visualizations
- 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
- examples.md - Usage examples with different data types
- reference.md - Detailed API documentation
- templates/ - Custom analysis templates
- tests/ - Test suite with fixtures