data-analysis

When to use

The user uploaded a data file (.csv / .tsv / .xlsx) or asks you to analyse, explore, or do EDA on a dataset.

How

1. update_plan with the steps (profile → interpret → recommend → optional deck).
2. Call analyze_data with the file path (and target if the user named a target column). Always run the tool — never guess at the data.
3. Read the report, then explain like a data scientist, in this order:
   • What it is — rows, columns, what each column represents.
   • Quality — missingness, likely outliers, dtype problems.
   • Signal — the strongest correlations / relationships to the target, and what they plausibly mean.
   • Recommendations — concrete next steps (cleaning, features, a model family worth trying).
4. If the user wants a presentation, pass the findings to make_slides.

Avoid

• Inventing numbers — every figure must come from the analyze_data output.
• Pasting the raw report back; synthesise it into insight.
• Claiming causation from correlation.

Done well

A short, structured read a stakeholder grasps in a minute: what the data says, what's trustworthy, and what to do next — every claim grounded in the tool's real numbers.