{"uid":"cap_UXXmYNcRD5K8DCBGh2u0F","slug":"numeric-precision-comparisons-record-quality-audit-string-3b1ab94a","name":"Numeric Precision Comparisons — Record Quality Audit, String Normalisation & Duplicate Key Report","description":"Numeric precision comparisons — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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-20-192?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}}},"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_oxz4CuSD3mGYiLlh4myNx","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 duplicate key detection) on supplied numeric source-vs-transformed value pairs, up to 200 rows per call.","exampleAgentPrompt":"Can you run the three data quality reports — record quality audit, string normalisation, and duplicate key check — on these 150 rows of source-to-transformed numeric values from my ETL pipeline? The rows have row_id, dataset_group, date, source_value, and transformed_value fields.","exampleUseCases":[{"title":"ETL pipeline precision validation","prompt":"I just ran my ETL job and ended up with 80 rows of source and transformed numeric values — can you audit them for record quality issues, check for string normalisation problems, and flag any duplicate row IDs or keys?"},{"title":"Data warehouse QA before load","prompt":"Before I load this batch of 200 records into the warehouse, can you run a quality audit, string normalisation check, and duplicate key report on the source_value and transformed_value columns grouped by dataset_group and date?"},{"title":"Investigating numeric drift in reporting","prompt":"I suspect my transformation logic is introducing numeric precision errors — can you compare these 60 source and transformed values across group-a and group-b and give me a record quality audit plus a duplicate key report?"}],"resultDescription":"Three independent analytical reports returned for the submitted rows: (1) a record quality audit flagging anomalies or issues in the numeric records, (2) a string normalisation report on value representations, and (3) a duplicate key report identifying repeated row_id or key combinations across the submitted dataset.","failureModes":["Rows array exceeds 200 items — request rejected","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 — validation error","Empty rows array — minimum item constraint violation"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, duplicate detection) on a batch of numeric source-vs-transformed records without requiring any external data lookups, live predictions, or professional assessments. It is ideal for ETL validation, data pipeline QA, and pre-load data checks where all data is self-contained and up to 200 rows per call is sufficient.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:43.585Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-string-3b1ab94a"}