{"uid":"cap_eJK2K2zW3et9QeBQaFb5u","slug":"dataset-storage-record-quality-audit-string-normalisation-exact-row-f90d4bb0","name":"Dataset Storage Record Quality Audit, String Normalisation & Exact Row Deduplication","description":"Dataset storage — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied dataset storage records (used_mb, row_count). 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-18-191?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","used_mb","row_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"used_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","used_mb":10,"row_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","used_mb":20,"row_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","used_mb":15,"row_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","used_mb":30,"row_count":4,"dataset_group":"group-b"}],"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_NV-o8re2UXAoU1-MJ5eNh","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":"Audits quality, normalises strings, and deduplicates rows in dataset storage records (used_mb, row_count) — three analytical reports per call, up to 200 rows.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and exact deduplication check on my dataset storage records? I have rows with fields like row_id, dataset_group, date, used_mb, and row_count — here are up to 200 rows I want analysed.","exampleUseCases":[{"title":"Pre-pipeline storage data cleanup","prompt":"I'm about to load our dataset storage logs into a pipeline and I want to make sure they're clean first — can you audit the record quality, normalise any inconsistent strings in the dataset_group field, and flag any exact duplicate rows? Here are the records with used_mb and row_count values."},{"title":"Monthly storage usage deduplication","prompt":"At the end of each month we export our storage usage table and there are often duplicate rows. Can you run exact deduplication plus a quality audit on this batch of up to 150 storage records, each with a date, row_id, dataset_group, used_mb, and row_count?"},{"title":"Data quality report for storage datasets","prompt":"I need a three-part report on these dataset storage records — record quality audit, string normalisation issues, and exact row duplicates. The rows have fields for row_id, dataset_group, date, used_mb, and row_count. Can you process all 80 of them?"}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit identifying missing, null, or out-of-range values in used_mb and row_count fields; (2) a string normalisation report flagging inconsistencies in dataset_group and other string fields; and (3) an exact row deduplication report identifying fully duplicate rows across the supplied dataset.","failureModes":["More than 200 rows submitted — request rejected at schema validation","Missing required fields (row_id, dataset_group, date, used_mb, or row_count) on any row — validation error","date field not matching YYYY-MM-DD pattern — schema rejection","used_mb or row_count outside allowed numeric range — schema rejection","Empty rows array — minItems violation"],"whenToPreferThis":"Choose this endpoint when you need a combined, low-cost ($0.01 USDC) batch analysis of dataset storage records covering all three dimensions — quality, string consistency, and exact deduplication — without needing live lookups, predictions, or external data enrichment. Ideal for pre-pipeline data preparation or periodic storage table hygiene checks on batches up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:57.428Z","isFirstParty":false,"canonicalSlug":"dataset-storage-record-quality-audit-string-normalisation-exact-row-f90d4bb0"}