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.
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.
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.
Lints a DNS zone file or a provider's CSV record export before it breaks web or mail - CNAME at the apex or next to other records, MX/NS pointing at a CNAME or IP, missing trailing dots, missing or duplicate SPF, +all/?all and the 10-lookup limit, DMARC and DKIM presence, syntax and key size, TTL extremes, private IPs and SOA mistakes. Returns corrected records. Includes an offline stdlib Python linter with --self-test.
