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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
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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
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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
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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
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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
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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
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Converts plain language data questions into structured SQL using CTEs, null safe logic, and readable column aliases. Use it when you want queries that run clean and stay readable f
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Paste in raw analysis bullets and figures to get a structured one page executive summary with a lead insight, quantified findings, owner assigned actions, and a risk flag. Use it b
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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
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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
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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.
chain-of-thoughttemplatedata-extractioncode-generationdata-analyticsGPT-4Claude 3.5 SonnetGemini 1.5 Pro
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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
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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