Agent-Skills.md

Agent Skills: Exploratory Data Analysis (EDA)

Exploratory Data Analysis for tabular data. Use when analyzing column distributions, checking data quality, examining class balance, detecting missing patterns, or generating summary statistics for datasets.

UncategorizedID: argythana/python-ml-skills/eda

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argythana/python-ml-skills
argythana

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pnpm dlx add-skill https://github.com/argythana/python-ml-skills/tree/HEAD/eda

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eda/SKILL.md

Skill Metadata

Name
eda
Description
Exploratory Data Analysis for tabular data. Use when analyzing column distributions, checking data quality, examining class balance, detecting missing patterns, or generating summary statistics for datasets.

Exploratory Data Analysis (EDA)

Analyze tabular datasets to understand distributions, data quality, and patterns.

When to Use

  • Understanding a new dataset before modeling
  • Checking data quality (missing values, outliers, duplicates)
  • Analyzing target variable distribution
  • Identifying class imbalance
  • Generating summary statistics

Analysis Process

  1. Connect to data - Verify access and inspect schema
  2. Analyze target variable first - Understand class balance
  3. Check each column - Distribution, missing data, cardinality
  4. Document findings - Save reports for reproducibility

Available Analyses

| Analysis | Description | |----------|-------------| | Column Distribution | Value counts, percentages, cardinality assessment | | Missing Data | Null counts, patterns (MCAR/MAR/MNAR) | | Class Balance | Imbalance detection for classification targets | | Summary Stats | Count, unique, nulls per column |

Column Distribution Analysis

For detailed analysis methodology and output format:

Quick Reference

Cardinality Levels: | Level | Criteria | Action | |-------|----------|--------| | Low | ≤10 unique | Good for categorical encoding | | Medium | 11-100 or <1% of rows | May need encoding strategy | | High | >100 and <50% of rows | Consider grouping/binning | | Very High | >50% of rows | Likely identifier, exclude |

Missing Data Thresholds: | Percentage | Assessment | |------------|------------| | 0% | No missing data | | <1% | Minimal - safe to drop or impute | | 1-5% | Some - consider imputation strategy | | >5% | Significant - investigate pattern |

Class Imbalance:

  • 80% in top class: Imbalance detected

  • 95% in top class: Extreme imbalance

Output Format

# Column Distribution: {column_name}

- **source**: path/to/data
- **column**: column_name

## Summary
- Total rows: N
- Null/missing: N (X%)
- Unique values: N
- Cardinality: Low|Medium|High|Very High

## Distribution
| Value | Count | Percentage | Cumulative |
|-------|-------|------------|------------|

## Observations
- Auto-generated insights

Best Practices

  1. Start with schema inspection before deep analysis
  2. Check target variable first for classification tasks
  3. Missing data may not be random - investigate patterns
  4. Save reports for reproducibility