Opinionated conventions for structuring, testing and shipping dbt analytics projects.
Reviews PostgreSQL and MySQL schema migrations (raw SQL or ORM-generated) for table locks, rewrites, data loss, and breaking changes, then proposes safe zero-downtime rewrites with a clear verdict.
Profiles CSV and spreadsheet exports before you trust them: infers column types and measures missing values, duplicates, mixed types, outliers, date format chaos, and key uniqueness with a tested Python script, then writes a prioritized data quality report with safe fixes.
Checks CSV time series (metrics, sensor readings, sales by hour) for data problems before you chart or alert on them: missing intervals, duplicate or out-of-order timestamps, empty values, flatlines from stuck exporters, robust median/MAD spikes, and counter resets. Use when a dashboard looks wrong, before training a forecast, or when asking "is this data complete?". Includes a stdlib Python checker with --self-test.
Explains every line of a .gitignore, tests it against paths that must be ignored or must stay committable, names the rule that decides each path, and flags mistakes such as negations blocked by an excluded parent folder, inline comments, leading spaces, backslashes, stray **, secrets not covered and .env.example ignored. Returns a fixed file. Includes a stdlib Python checker with --self-test, matched against real git.
Catches quality drops when you change an AI feature's prompt or model: runs your test cases, compares old and new answers side by side, and tracks cost and speed.
Watches your training runs: summarises loss curves and metrics, detects divergence, NaNs and stalled GPUs early, and writes a comparison when runs finish.
Tracks SLO error budgets across services and tells you when burn rate means it is time to freeze risky deploys.
Turns your dashboard numbers into a short weekly story of what changed and why.
