{"uid":"cap_KoyG1aOjRVRbr2njpnbfn","slug":"numeric-precision-comparisons-record-quality-audit-adjacent-row-changes-a99c1c2c","name":"Numeric Precision Comparisons — Record Quality Audit & Adjacent Row Changes","description":"Numeric precision comparisons — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes 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-20-011?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_1nJGyfaQDT6Nz8wA0aZYf","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 analytical reports (record quality audit and adjacent row changes) on up to 200 rows of numeric precision comparison data (source_value vs transformed_value), using local analytical engines with no external lookups.","exampleAgentPrompt":"Run a record quality audit and adjacent row changes analysis on my transformation data — I have 4 rows in groups 'group-a' and 'group-b' dated 2026-10-01 through 2026-10-04, with source values [10, 20, 15, 30] and transformed values [1, 2, 3, 4], and I want to analyze the source_value field.","exampleUseCases":[{"title":"ETL pipeline precision drift check","prompt":"I need to audit my ETL output for numeric precision issues — here are 50 rows from my 'sales_pipeline' dataset with original source values and the transformed values after my processing step, all dated this month. Run both the record quality audit and the adjacent row changes report on the source_value field."},{"title":"Data transformation QA before reporting","prompt":"Before I send this financial report, can you check these 120 rows of transformed figures against their source values for quality issues and any unusual jumps between adjacent rows? The dataset group is 'Q3-financials' and dates run from 2025-07-01 to 2025-09-30 — analyze the transformed_value field."},{"title":"Detecting anomalous row changes in sensor data","prompt":"I have 200 rows of sensor readings where each row has a raw sensor value and a calibrated transformed value, grouped under 'sensor-array-7' with daily dates in 2025. Run the adjacent row changes and record quality audit reports on the source_value field so I can spot any calibration anomalies."}],"resultDescription":"Two independent analytical reports: (1) a record quality audit highlighting data quality issues across the supplied rows (e.g. nulls, outliers, precision anomalies), and (2) an adjacent row changes report showing how values shift between consecutive rows — all computed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes rejection","Date strings not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array (minItems: 1 violated) returns an error","Payment of 0.01 USDC required per call; unpaid requests are rejected"],"whenToPreferThis":"Choose this endpoint when you need two bundled analytical reports — record quality audit plus adjacent row changes — on a batch of up to 200 rows containing paired source/transformed numeric values, and you want local computation with no external data dependencies. It is cost-effective at $0.01 USDC for both reports simultaneously, making it ideal for ETL validation, data pipeline QA, or precision drift checks where you already have the data in hand.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:46.261Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-adjacent-row-changes-a99c1c2c"}