{"uid":"cap_8ZWcbzVjmWsx5Tq3PBgvr","slug":"numeric-precision-comparisons-record-quality-audit-string-34625c33","name":"Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Keyed Dataset Comparison","description":"Numeric precision comparisons — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied numeric precision comparisons records (source_value, transformed_value). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 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three independent analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on supplied numeric precision comparison rows containing source and transformed values, for up to 200 rows per call.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation check, and keyed dataset comparison on these two sets of numeric rows — the first set has source values like 10, 20, 15, 30 and the second set has the same row IDs but slightly different source values like 10, 21, 17, 30, both dated from 2026-10-01 through 2026-10-04 across groups A and B?","exampleUseCases":[{"title":"ETL pipeline numeric accuracy check","prompt":"I just ran an ETL transformation on our sales figures and I want to compare the original source values against the transformed output for 150 rows across two product groups — can you run the record quality audit, string normalisation, and keyed dataset comparison reports to find any precision issues or mismatches?"},{"title":"Data migration validation across environments","prompt":"We migrated numeric records from our old system to the new one and I need to audit the quality of 80 rows, normalise any string inconsistencies, and do a keyed comparison between the pre-migration and post-migration values grouped by region and dated from 2025-01-01 to 2025-03-31."},{"title":"Financial data transformation QA","prompt":"Can you audit these 60 rows of financial data where each row has a source_value from our ledger and a transformed_value after our rounding process — I need the record quality audit, string normalisation report, and keyed comparison to spot any rows where the transformation introduced unexpected precision changes?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit highlighting data quality issues in the supplied rows, (2) a string normalisation report identifying normalisation concerns, and (3) a keyed dataset comparison report showing discrepancies between the primary and secondary datasets — all computed locally without external lookups.","failureModes":["More than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) — validation error","Date field not matching YYYY-MM-DD format — schema validation failure","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — rejection","Empty rows array — minItems violation","Payment not processed — endpoint not accessible"],"whenToPreferThis":"Choose this endpoint when you need three bundled data quality reports — record quality audit, string normalisation, and keyed dataset comparison — on up to 200 rows of numeric source-vs-transformed pairs in a single low-cost call. It is ideal for validating ETL pipelines, data migrations, or numeric transformation processes where you want to compare before/after values across named dataset groups without any external lookups or live data fetching.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:00.352Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-string-34625c33"}