eda-explorerlisted

# EDA Explorer A reproducible exploratory data analysis skill. Same dataset → same report structure → no surprises. ## When to use this skill Activate this skill whenever the user wants to **understand a tabular dataset** before doing anything else with it. Typical signals: - User uploads a `.csv`, `.xlsx`, `.parquet`, or `.tsv` file - User asks "what's in this data?", "explore this", "do an EDA", "fais une exploration" - User mentions a dataset path or DataFrame variable and asks to "profile" it - User mentions wanting to understand the shape, quality, or distributions **Do NOT activate this skill for:** - Pure modeling questions (use feature-engineer or model-trainer instead) - Quick one-off questions about a single column (just answer directly) - Datasets with > 1M rows without sampling first ## Workflow For every dataset, follow these steps in order: 1. **Load & inspect** — read the file with the right pandas reader, check shape and dtypes 2. **Run all 9 sections** of the standard report (below) — never skip a section 3. **Output as a single markdown report** with clear `##` headers per section 4. **End with concrete recommendations**, not generic advice ## Standard EDA Report Structure The report **must** have exactly these 9 sections, in this order: ### 1. Dataset Overview - File name, file size on disk, file format - Shape: `(n_rows, n_cols)` - Memory footprint (`df.memory_usage(deep=True).sum()` in MB) - A small `head(5)` preview ### 2. Schema & Types - Ta