phoebefu6

how-to-data-quality

Put a data-quality gate on a warehouse load - map every defect to one of the six DQ dimensions (completeness, validity, consistency, timeliness, uniqueness, accuracy), run a real FAIL -> fix -> PASS cycle with a quarantine ledger that reconciles every row, and generate a self-contained executive DQ scorecard where every number is computed. Use when asked to "check data quality", "validate this load", "build DQ checks", "data quality scorecard", "should we trust this data", "set up a quality gate", "quarantine bad rows", or when a pipeline needs defects caught at ingestion instead of in a board meeting. Walks the 6-step pipeline - input, sample data, objective, find-skills, build (gate + fixes + scorecard), expert review.

how-to-eda-codex

Run a trust-first exploratory data analysis (EDA) with Codex + Python on any schema, assembling an open-source profiling / integrity / forensic toolbox before writing code. Use when handed a new dataset from an unproven pipeline and asked to "can we trust this data", "profile this", "run EDA", "check data quality", "find integrity issues", or before building any dashboard/model on unfamiliar tables. Walks the same 6-step pipeline as how-to-eda (the Claude edition) but leads with referential integrity, entity resolution, and forensic checks - and outputs findings ranked by dollar and trust impact.

how-to-eda

Run a decision-first exploratory data analysis (EDA) with Claude + Python on any schema. Use when handed a data schema or new dataset and asked to "explore the data", "run EDA", "profile this", "what's in this data", "can we trust this data", or before building any dashboard/model on unfamiliar tables. Walks the 6-step pipeline - input, sample data (if no real data yet), objective, find-skills, code with real executed charts, expert review - and outputs findings ranked by dollar impact.

how-to-metric-to-scorecard

Turn raw marts into a board-ready executive scorecard - define metrics explicitly, validate each against the naive version an analyst ships by accident, then generate a self-contained HTML scorecard where every number is computed, not hand-typed. Use when asked to "build a scorecard", "executive/board dashboard", "KPI report", "define our metrics", "revenue dashboard", "what do we put in front of the board", "metric definitions", or when a stakeholder wants numbers they can trust and audit. Walks the 6-step pipeline - input, sample data, objective, find-skills, build (metric definitions + validation + HTML scorecard), expert review - and outputs an auditable scorecard, not a pile of aggregates.

how-to-rfm

Run a decision-first RFM customer segmentation with Claude + Python on any transaction history. Use when handed customer/order data and asked to "segment customers", "run RFM", "who are our best customers", "who is churning", "where should we spend retention/CRM budget", "find high-value customers", or before designing any lifecycle/loyalty/win-back campaign. Walks the 6-step pipeline - input, sample data (if none yet), objective, find-skills, code with real executed charts, expert review - and outputs segments ranked by dollar at stake with a budget recommendation.

Code & Development Listed

how-to-sales-forecasting

Forecast seasonal demand honestly and turn it into a signable inventory buy - run a data-quality gate before measuring anything, separate what the data can prove (weekly seasonality, backtested accuracy) from what it cannot (a peak observed once), then price the unprovable part as a costed newsvendor decision instead of a fake confidence interval. Use when asked to "forecast sales/demand", "how much inventory for <month>", "plan the seasonal buy", "predict next quarter's volume", "is this spike real", or when a forecast must survive a finance review. Walks the 6-step pipeline - input, sample data, objective, find-skills, build (DQ gate + backtests + spike sizing + Monte Carlo + costed commitment), expert review - and outputs a buy paper, not a point estimate.

AI & Automation Listed

how-to-schema-and-warehouse

Turn a raw operational data dump into a queryable star-schema warehouse with a data contract, using DuckDB + pandera. Use when handed raw exports from one or more source systems and asked to "design a schema", "build a warehouse", "model this data", "set up a data lake / warehouse", "create a star schema", "reconcile these sources", or before any analytics/BI is built on unmodeled data. Walks the 6-step pipeline - input, sample data, objective, find-skills, build (ERD + DDL + contract + real queries), expert review - and outputs a genuinely queryable warehouse, not just a diagram.

API & Backend Listed

how-to-schema-consolidation

Audit a sprawling multi-server data estate (many cloned schemas that have drifted) and safely consolidate the copies of a table into one, using a column-level schema diff. Use when handed many servers/schemas that each cloned the same tables, or asked to "consolidate schemas", "merge duplicate tables", "compare schemas across servers", "schema diff", "which columns differ across environments", "dedupe tables", "measure data dictionary coverage", or before merging any table that exists in more than one database. Walks the 6-step pipeline - input, sample data, objective, find-skills, build (coverage + redundancy + drift audit, schema-diff matrix, merged superset DDL), expert review - and never merges on the intersection.