{"uid":"cap_weU9NOISZrXSkNp_sR91p","slug":"numeric-precision-comparisons-record-quality-audit-string-f2da1c91","name":"Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Date by Category Pivot","description":"Numeric precision comparisons — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-206?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_EyPqkDzhs8V21gBQlDX2O","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs three local analytical reports (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of numeric precision comparison data containing source and transformed values.","exampleAgentPrompt":"Can you run the three-report bundle — record quality audit, string normalisation, and date-by-category pivot — on my numeric precision comparison rows? Here are up to 50 rows each with a row_id, dataset_group like 'group-a', a date in YYYY-MM-DD format, a source_value, and a transformed_value; use source_value as the field to focus on.","exampleUseCases":[{"title":"ETL pipeline transformation audit","prompt":"I just ran an ETL job that transformed our source numeric values and I want to check the quality. Can you audit up to 150 rows of my source_value vs transformed_value records, normalise any string fields, and give me a pivot by date and category so I can spot where the transformation went wrong?"},{"title":"Data quality check on grouped records","prompt":"We have two dataset groups — 'group-a' and 'group-b' — and I need a record quality audit plus a date-by-category pivot on about 80 rows of our precision comparison data. Each row has a date, row_id, source_value, and transformed_value. Can you run all three reports at once?"},{"title":"Vendor-supplied data normalisation review","prompt":"A vendor sent us 120 rows of numeric comparison data with source and transformed values dated across October 2026, and I need to check record quality, normalise the string fields, and see a pivot of the values by date and category group before we load it into our system."}],"resultDescription":"Three independent analytical reports computed locally on the supplied rows: (1) a record quality audit flagging completeness and anomalies, (2) a string normalisation report standardising text fields, and (3) a date-by-category pivot summarising numeric values grouped by date and dataset_group. All analysis is performed on the submitted data with no external lookups.","failureModes":["Empty or missing rows array returns a validation error","More than 200 rows submitted — exceeds maxItems limit","Row missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes schema rejection","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric values outside ±1,000,000,000,000 range are rejected","Invalid field enum value (not 'source_value' or 'transformed_value') causes rejection","Payment of 0.01 USDC not settled — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports — record quality audit, string normalisation, and date-by-category pivot — on a batch of up to 200 numeric precision comparison rows in a single low-cost call. It is ideal for ETL validation, data quality checks before ingestion, or post-transformation audits where source and transformed values need to be compared. Prefer it over single-report endpoints when all three report types are relevant and cost efficiency matters. Not suitable when you need live data lookup, predictions, or external integrations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:42.083Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-string-f2da1c91"}