analytics-with-claude-code

Analyze and optimize slow SQL queries. Use when the user says a query is slow, asks to optimize or speed up SQL, wants to find anti-patterns, needs index recommendations, or asks for a query rewrite. Also use when EXPLAIN output shows full table scans or poor join strategies.

Generate structured analytics reports with metrics, trends, and visualizations. Use when the user asks for a business review, monthly report, executive summary, deep dive, incident postmortem, or any deliverable that combines data, charts, and narrative for stakeholders.

Perform rigorous A/B test analysis with statistical significance testing, sample size validation, and ship/no-ship recommendations. Use when the user mentions A/B tests, experiments, variant analysis, significance testing, sample size planning, or asks "should we ship this?" based on experiment data.

Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.

Perform comprehensive Exploratory Data Analysis on any dataset. Use when the user mentions a new dataset, says "explore this data", "profile this table", "what does this data look like", uploads a CSV/Parquet file, or needs to understand distributions, nulls, correlations, and outliers before deeper analysis.

Explain complex SQL queries in plain English with Mermaid data flow diagrams, performance annotations, and anti-pattern detection. Use when the user pastes a SQL query and asks "what does this do?", "explain this query", or needs to understand inherited SQL, CTEs, window functions, recursive queries, or dbt model logic.

Calculate standard business metrics: retention, LTV, CAC, churn, conversion funnels, growth rates, MRR, NRR, DAU/MAU. Use when the user asks for a specific KPI definition, needs a retention curve, LTV calculation, funnel analysis, or growth rate computation. Provides both SQL templates and Python implementations.

Compare two metric definitions that should produce the same number and find exactly where they disagree. Use when the user says "these numbers don't match", "why do two dashboards show different results", or when migrating metric logic and validating the new query against the old one.

4-phase structured debugging with a 3-strike escalation rule. Use when a query fails, a pipeline breaks, results look wrong, a dbt model errors, or any analytical code produces unexpected output. Prevents cargo-cult debugging by enforcing reproduce → analyze → fix → verify in strict order.

Generate recurring weekly or monthly analytics reports with period-over-period comparison, anomaly detection, and executive summaries. Use when the user asks for a weekly report, monthly KPI review, recurring metrics snapshot, or needs automated period-over-period diffing. Saves templates for one-command re-runs.