Creates a ready to run Python script for exploratory data analysis that covers missing value detection, univariate and bivariate exploration, and automatic chart generation. Source
Creates a ready to run Python script for exploratory data analysis that covers missing value detection, univariate and bivariate exploration, and automatic chart generation. Source
Walks an LLM through a full hypothesis test: checking assumptions, choosing the right test, computing effect size, and explaining the result in plain language, with runnable code i
Prompts the AI to critique your analysis like a tough peer reviewer, probing methodology, hidden assumptions, confounders, and whether the data actually backs your conclusions. Use
Generates a SQL query that builds a cohort by month offset retention matrix, with clear metric definitions and commented CTEs. Useful for product and data teams tracking how user c
Walks you through the full exploratory data analysis pipeline in six chained steps, covering data profiling, distributions, correlations, and a final written summary report. Use it
Paste your A/B test results and get a structured evaluation covering statistical significance, effect size, segment differences, and novelty risk. Outputs a clear ship, kill, or ke
Paste any stakeholder question and a schema description to get correct SQL, a plain English explanation of what the query returns, and edge cases the business user should know abou
Paste your A/B test results and get a rigorous evaluation covering statistical significance, power analysis, effect size, and segment breakdowns. Flags novelty effect risk and comm
Generates a modular Python DataCleaner class where each data issue gets its own method, all steps chain together, and every run logs what changed alongside before/after row counts.
Generates a weekly cohort retention query with a full date spine so zero activity users appear as 0% retention rather than being dropped as NULL. Use this when building retention d
Generates CTE structured SQL from your actual table schemas and a stated business question, with documented assumptions and clean naming conventions. Use it when you need analytica