About stat-eda
stat-eda is an exploratory data analysis skill for analysts and modelers. It emphasizes splitting data before exploration and using only the training set to reduce leakage, while highlighting checks for missingness, subgroup trends, and suspicious features.
Key features
- 01Split data before EDA
- 02Inspect missing-data patterns
- 03Stratify trends by likely confounders
- 04Flag potentially leaky features
Use cases
- 01Explore training data before modeling
- 02Assess whether missing values are random
- 03Check whether subgroup trends reverse overall findings
- 04Review features highly correlated with a target
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/stat-edain Claude Code to confirm it loaded.
Bundled filesexamples/sample_scenario.mdreferences/missing-data.md
Details
From the project READMEExploratory Data Analysis (EDA)
Framework
IRON LAW: Perform EDA Only AFTER Train/Test Split — Or You Leak the Future
Agents know "do EDA first." But they almost always do EDA on the FULL
dataset before splitting. This is information leakage: you've seen the
test set's distributions, outliers, and correlations, and your subsequent
modeling choices (feature scaling, outlier treatment, imputation strategy)
are now informed by data the model shouldn't see. Split first, then EDA
only on the training set. Apply the same transformations to the test set
without re-examining it.
Exception: data quality checks (nulls, dtypes, duplicates) CAN run on
the full dataset since they don't inform model hyperparameters.
EDA Workflow
Standard five-phase flow (structure → quality → univariate → bivariate → findings summary). Assume the agent already knows these steps. Focus on the non-obvious traps below instead.
Critical additions most EDA guides miss:
- Split BEFORE explore (see IRON LAW above)
- Missing data pattern matters more than count: MCAR is safe to impute; MNAR (e.g. high-income respondents skip income question) requires domain modeling, not mean-fill
- Simpson's paradox check: If a trend holds in the aggregate but reverses within subgroups, the aggregate trend is misleading. Always stratify by the most obvious confound before reporting a bivariate finding
- Data leakage in features: A feature that perfectly correlates with the target is usually derived FROM the target (e.g. "refund_amount" predicting churn — it's an effect, not a cause). Flag any feature with r > 0.95 for causal review
For the visualization selection guide, see references/missing-data.md.
Output Format
# EDA Report: {Dataset Name}
## Dataset Overview
- Rows: {N}, Columns: {N}
- Date range: {if applicable}
- Key columns: {description}
## Data Quality
| Issue | Columns Affected | Count/% | Action |
|-------|-----------------|---------|--------|
| Missing values | {cols} | {N / %} | {drop / impute / investigate} |
| Outliers | {cols} | {N} | {cap / remove / keep} |
| Duplicates | — | {N} | {remove} |
## Key Statistics
| Variable | Mean | Median | Std | Min | Max | Distribution |
|----------|------|--------|-----|-----|-----|-------------|
| {var} | ... | ... | ... | ... | ... | {normal/skewed/bimodal} |
## Key Findings
1. {insight with supporting data}
2. {insight}
3. {insight}
## Recommendations
- {next analysis step or data issue to resolve}
Gotchas
- Correlation ≠ causation: EDA finds associations. Establishing causation requires controlled experiments or causal inference methods.
- Outliers can be data errors OR real signal: Don't auto-remove. Investigate. A transaction amount of $1M might be a typo or your biggest customer.
- Missing data has meaning: Data missing from one column may be related to values in another. "Missing income" may mean "unemployed", not random. Check patterns.
- Visualization lies: Truncated Y-axes, cherry-picked time ranges, and misleading scales can distort insights. Always use appropriate scales and note limitations.
References
- For missing data handling strategies, see
references/missing-data.md
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