EDA Report Builder

    1

    Profile any dataset systematically: structure, nulls, outliers, distributions and correlations, with a findings summary.

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    EDA Report Builder

    Example session with this skill installed

    Profile subscriptions.parquet before I build a churn model. One row = one customer subscription. Skip the email column (PII). Tell me the top issues and whether it's ready for modelling.

    • Read your context and instructions
    • Compiled the eda report builder

    Illustrative example on synthetic data. It follows this skill's findings_summary.md template.

    EDA Findings Summary: subscriptions

    Dataset at a Glance

    ItemValue
    Rows × columns48,210 × 13 (email skipped)
    Grainone row per subscription ✅ confirmed; subscription_id is unique
    Date range2023-01-02 → 2026-09-28
    Memory6.1 MB

    Top Quality Issues

    Issue 1: cancel_reason is 71% null

    This is expected for active subscriptions, but 9% of

    cancelled rows are also null. Treat those as "unknown" rather than dropping them.

    Issue 2: Outliers in monthly_price

    312 rows are above the IQR fence ($480+). 290 of them are enterprise plans (real signal). 22 have a price of $99,999, which is a placeholder value and a data error.

    Issue 3: Redundant features

    seats and monthly_price have r = 0.91. Keep one, or combine them, before fitting a linear model.

    What Looks Good

    • No duplicate subscriptions and no negative prices
    • plan_tier has 4 clean categories with no stray casing
    • Signup volume is stable month to month, with no missing months

    Next Step

    Ready for modelling after 2 fixes: remove the 22 placeholder prices, and decide how to handle seats versus monthly_price. The full per-column stats are in eda_report_template.md.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    EDA Report Builder gives your agent a repeatable exploratory data analysis routine, so you never skip a step before real analysis starts. It confirms the grain, profiles nulls against thresholds, flags outliers with IQR and z-score, summarises distributions, and highlights highly correlated pairs. It then works through an EDA checklist and writes both a full profiling report and a short summary of the top issues.

    Use it when

    • You just received a new dataset and need to know what is in it
    • An analysis produced surprising numbers and you want to check the data first
    • Someone asks "is this data reliable?"
    • You are about to fit a model or run a statistical test

    What's inside (installs as one folder, programmatic-eda/):

    • programmatic-eda/LICENSE
    • programmatic-eda/SKILL.md
    • programmatic-eda/assets/eda_report_template.md
    • programmatic-eda/assets/findings_summary.md
    • programmatic-eda/references/eda_checklist.md
    • programmatic-eda/references/pandas_polars_recipes.md
    • programmatic-eda/references/quality_thresholds.md
    • programmatic-eda/scripts/correlation_explorer.py
    • programmatic-eda/scripts/data_overview.py
    • programmatic-eda/scripts/distribution_summary.py
    • programmatic-eda/scripts/null_profiler.py
    • programmatic-eda/scripts/outlier_detector.py

    Please read before buying: this is packaged from my free, MIT-licensed open-source library (https://github.com/nimrodfisher/data-analytics-skills). The same content is available there for free. You are paying for a ready-to-install package. It is a one-time purchase, sold as-is, with no support, updates or maintenance included. The MIT license is included.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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