{"uid":"cap_zDHFkU9VzPhAEkE6Ve8Y7","slug":"numeric-precision-comparisons-record-quality-audit-keyed-dataset-c2cfd9e4","name":"Numeric Precision Comparisons — Record Quality Audit & Keyed Dataset Comparison","description":"Numeric precision comparisons — Record quality audit + Keyed dataset comparison: Record quality audit + Keyed dataset comparison for supplied numeric precision comparisons records (source_value, transformed_value). Up to 200 rows; 2 independent reports using shared local analytical engines. 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Each row has a row_id, dataset_group, date, source_value, and transformed_value — I want to see both reports to check if my transformation pipeline is introducing any precision errors.","exampleUseCases":[{"title":"ETL pipeline numeric drift detection","prompt":"I just ran my ETL pipeline and I need to audit 120 rows of source and transformed values across two dataset groups. Can you run both the record quality audit and the keyed dataset comparison reports on them so I can see where numeric precision is being lost?"},{"title":"Financial data transformation validation","prompt":"Before we sign off on this month's financial close, can you compare our 80 source ledger values to the transformed values and give me both a quality audit and a keyed comparison report to confirm there are no precision mismatches?"},{"title":"Cross-dataset version discrepancy check","prompt":"We have two versions of our sensor dataset — the original readings and the processed outputs — can you run a keyed dataset comparison and quality audit on all 150 rows so we can identify which rows have numeric differences between the two versions?"}],"resultDescription":"Two independent analytical reports: (1) a record quality audit summarizing data completeness, null counts, and numeric anomalies across the supplied rows, and (2) a keyed dataset comparison report identifying discrepancies between source_value and transformed_value fields, grouped by dataset_group and keyed by row_id. No external lookups or predictions are made; all analysis is performed on the supplied data only.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes row rejection","Values outside ±1,000,000,000,000 range are rejected by schema validation","Invalid date format (not YYYY-MM-DD) causes parsing failure","Empty rows array (minItems:1 not met) returns an error","Payment failure via x402 protocol prevents execution"],"whenToPreferThis":"Choose this endpoint when you need both a record quality audit AND a keyed dataset comparison in a single call for numeric source/transformed value pairs. It is ideal for validating ETL or data pipeline outputs, checking numeric precision loss, or comparing two versions of the same dataset by row key — all without requiring any external data lookup or live system access. 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