Data analysisMaintained by the TabTin teamOpen source

Data Analysis Toolkit

Audit data quality, detect outliers, run regressions, and evaluate A/B tests with clear evidence.

WHAT YOUR TEAM CAN DO

What this Pack helps your team accomplish

Agent calls these capabilities when it recognizes the matching job to be done

Audit tabular data for missing values, duplicates, outliers, type conflicts, and format problems, then propose fixes.

Scan CSV data with Z-score, IQR, and moving-average methods, separating explainable anomalies from items to investigate.

Run linear or logistic regression and explain coefficients, fit, significance, and collinearity in practical language.

Evaluate an A/B test with conversion lift, significance, confidence intervals, power, and sample-size guidance.

INSIDE THE PACK

01
Dataset health audit
Audit tabular data for missing values, duplicates, outliers, type conflicts, and format problems, then propose fixes.
02
Outlier scan
Scan CSV data with Z-score, IQR, and moving-average methods, separating explainable anomalies from items to investigate.
03
Regression insight
Run linear or logistic regression and explain coefficients, fit, significance, and collinearity in practical language.
04
Split test evaluator
Evaluate an A/B test with conversion lift, significance, confidence intervals, power, and sample-size guidance.

BUILT IN THE OPEN

Inspect each Skill’s guidance, trigger conditions, and implementation on GitHub as the community continues to improve it.