{"uid":"cap_jGiE1LoVP1lcjbauV72NQ","slug":"dataset-storage-record-quality-audit-string-normalisation-adjacent-d673e46a","name":"Dataset Storage Record Quality Audit, String Normalisation & Adjacent Row Changes","description":"Dataset storage — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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-18-200?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},"field":{"enum":["used_mb","row_count"],"type":"string","default":"used_mb"}}},"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_RG4SmXlJ-LKlYh2ybM6WT","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 analytical reports on up to 200 dataset storage records: a record quality audit, string normalisation, and adjacent row change detection for used_mb or row_count fields.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and adjacent row change check on these dataset storage records — the rows have fields like date, used_mb, and row_count — and focus the change analysis on used_mb?","exampleUseCases":[{"title":"Storage usage anomaly detection","prompt":"I have a list of up to 50 dataset storage records with dates, group names, and used_mb values — can you run the quality audit and adjacent row change reports on them so I can spot any sudden spikes in storage usage?"},{"title":"Dataset group name normalisation","prompt":"My dataset storage logs have inconsistent group name strings across about 80 rows. Can you run the string normalisation report on them so I get standardised, clean group names?"},{"title":"Row count consistency audit","prompt":"I want to check the quality and row-to-row changes in row_count across 120 dataset storage records I have — can you run all three reports focusing on the row_count field?"}],"resultDescription":"Returns three independent analytical reports based solely on the supplied data: (1) a record quality audit highlighting missing, null, or out-of-range values; (2) a string normalisation report for text fields like dataset_group; and (3) an adjacent row changes report showing differences between consecutive rows for the selected numeric field (used_mb or row_count). No external data or predictions are included.","failureModes":["More than 200 rows supplied — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","field enum value not 'used_mb' or 'row_count' causes validation failure","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical perspectives (quality audit, string normalisation, adjacent changes) on a small-to-medium batch of dataset storage records (up to 200 rows) in a single low-cost call. It is a pure local computation with no live lookups or external side effects, making it safe and deterministic. Prefer alternatives if you need real-time storage monitoring, predictive analytics, or analysis of more than 200 rows at once.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:18.783Z","isFirstParty":false,"canonicalSlug":"dataset-storage-record-quality-audit-string-normalisation-adjacent-d673e46a"}