{"uid":"cap_EoPG0Q7y40txBVNjMGenu","slug":"numeric-precision-comparisons-record-quality-audit-date-by-category-dd2037c7","name":"Numeric Precision Comparisons — Record Quality Audit & Date-by-Category Pivot","description":"Numeric precision comparisons — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot for supplied numeric precision comparisons records (source_value, transformed_value). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-017?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_Gx40Vb5pGNBVAAtpyDtie","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 two local analytical reports on supplied numeric source/transformed value pairs: a record quality audit and a date-by-category pivot, for up to 200 rows.","exampleAgentPrompt":"Audit the quality of my numeric transformation records and also give me a date-by-category pivot — here are 4 rows: row-1 on 2026-10-01 in group-a with source_value 10 and transformed_value 1, row-2 on 2026-10-02 in group-b with source 20 and transformed 2, row-3 on 2026-10-03 in group-a with source 15 and transformed 3, row-4 on 2026-10-04 in group-b with source 30 and transformed 4; use source_value as the primary field.","exampleUseCases":[{"title":"ETL pipeline numeric accuracy audit","prompt":"I just ran an ETL job and want to check data quality — can you audit these 10 records for me? Each row has a row_id, dataset_group, date, source_value, and transformed_value. Flag any quality issues and also pivot the source_value totals by date and group."},{"title":"Transformation precision drift detection","prompt":"I have 50 rows from my data transformation run — source values are the raw sensor readings and transformed values are after my scaling function. Give me a record quality audit plus a date-by-category breakdown so I can see if any categories are drifting, using transformed_value as the field to pivot."},{"title":"Monthly batch data quality review","prompt":"At the end of each month I need a quality audit of my dataset plus a date-grouped category pivot — here are 150 rows with row_ids, groups, dates in YYYY-MM-DD format, and both source and transformed numeric values. Run both reports and tell me which records have quality problems."}],"resultDescription":"Two independent analytical reports returned together: (1) a record quality audit flagging issues in the supplied source_value/transformed_value pairs (e.g. nulls, out-of-range, mismatches), and (2) a date-by-category pivot summarizing the selected field (source_value or transformed_value) grouped by date and dataset_group. No external lookups or predictions are made — all analysis is local to the supplied rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Row missing required fields (row_id, dataset_group, date, source_value, transformed_value) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric values outside ±1,000,000,000,000 range — rejected","Invalid field enum value (must be 'source_value' or 'transformed_value') — error returned","Payment of 0.01 USDC not included or fails — 402 response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of paired numeric source/transformed data and need both a record quality audit and a date-by-category pivot in a single cheap call ($0.01 USDC). It is best suited for ETL validation, transformation accuracy checks, and batch data quality reviews where no external data, live lookups, or predictions are needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:34.475Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-date-by-category-dd2037c7"}