Data explore

Skill giacomogaglione/claude-awesome-stack/stacks/python-ml/skills/data-explore

Installable stack packs for Claude Code — production-ready skills, hooks, and project configs for domain-specific development

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npx -y skills add giacomogaglione/claude-awesome-stack --skill data-explore

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Profile and explore datasets -- schema inference, distributions, missing values, outliers, correlations. Use when starting work with a new dataset or investigating data quality issues.

SKILL.md

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Data Exploration Skill

When exploring a dataset, follow this structured approach. Adapt based on whether the data is tabular (CSV/DataFrame), image-based, or text.

1. Schema and Structure

First, understand what you're working with:

Load a sample (first 5 rows + last 5 rows)
Column names, dtypes, and count of non-null values
Dataset dimensions (rows x columns)
Memory usage
Identify the target variable if this is a supervised learning task

For tabular data:

df.info()
df.describe(include='all')
df.head()
df.dtypes.value_counts()

2. Missing Values

Map the missing data landscape:

Count and percentage of missing values per column
Pattern analysis: are values Missing Completely At Random (MCAR), Missing At Random (MAR), or Missing Not At Random (MNAR)?
Identify columns with >50% missing (candidates for dropping)
Check if missingness correlates with the target variable

missing = df.isnull().sum()
missing_pct = (missing / len(df) * 100).round(2)
missing_report = pd.DataFrame({'count': missing, 'pct': missing_pct})
missing_report[missing_report['count'] > 0].sort_values('pct', ascending=False)

3. Distribution Analysis

For each feature, characterize its distribution:

Numerical features:

Min, max, mean, median, std
Skewness and kurtosis
Identify if log-transform would help (right-skewed data)

Categorical features:

Cardinality (number of unique values)
Value counts for top-10 categories
Identify rare categories (<1% frequency)

4. Outlier Detection

Flag potential outliers:

IQR method: values below Q1 - 1.5IQR or above Q3 + 1.5IQR
Z-score method: values with |z| > 3
Domain-specific checks (e.g., negative ages, future dates)

Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df[col] < Q1 - 1.5 * IQR) | (df[col] > Q3 + 1.5 * IQR)]

5. Correlations and Relationships

Identify feature relationships:

Pearson correlation matrix for numerical features
Flag highly correlated pairs (|r| > 0.8) as candidates for feature selection
Check correlation with target variable
For categorical features, use chi-squared test or Cramer's V

6. Data Quality Score

Summarize findings with a quality assessment:

Completeness: % of non-null values across all cells
Uniqueness: % of columns with no duplicates (where expected)
Consistency: any type mismatches or encoding issues
Validity: % of values within expected ranges

Present the final summary as a structured report.