Compares two CSV exports of the same records (old vs new, billing vs ledger, source vs warehouse) by key after normalizing headers, delimiters, currency formats, accounting negatives, dates and case. Reports added, removed and duplicate keys, key format drift, changed values per column with tolerances, possible re-keys and a control-total bridge, then writes a reconciliation report. Includes a stdlib Python checker with --self-test.
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.
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.
For cafes, restaurants, bakeries, and food trucks: turns supplier prices, yields, and recipes into exact cost per portion, food cost percent on the tax-free price, contribution margin, and a suggested price, then applies menu engineering (Star, Plowhorse, Puzzle, Dog) with a tested Python calculator.
Builds fixed-rate loan and mortgage amortization schedules, shows how much of each payment goes to interest, and compares payoff strategies such as extra monthly payments, one-off lump sums, a higher fixed payment, or a shorter term, with interest saved, months saved, payoff date, and an APR estimate when fees apply. Use when a user asks "how much interest will I pay?", "should I overpay my mortgage?", "what if I pay 200 more a month?", or wants a payoff plan for a car, student, or home loan.
