{"uid":"cap_cYUDEelEZcuLZZ8TMCulS","slug":"dataset-storage-analytics-record-quality-audit-string-normalisation-92cbd94b","name":"Dataset Storage Analytics — Record Quality Audit, String Normalisation & Date-by-Category Pivot","description":"Dataset storage — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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-18-206?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_yCa6GKigFwDwyVrox4oTi","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 (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of dataset storage records containing used_mb and row_count fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and date-by-category pivot on my storage dataset? I have 4 rows: row-1 on 2026-10-01 with used_mb=10 and row_count=1 in group-a, row-2 on 2026-10-02 with used_mb=20 and row_count=2 in group-b, row-3 on 2026-10-03 with used_mb=15 and row_count=3 in group-a, and row-4 on 2026-10-04 with used_mb=30 and row_count=4 in group-b — use used_mb as the analysis field.","exampleUseCases":[{"title":"Storage usage data quality check","prompt":"I have 50 rows of dataset storage records with row_id, dataset_group, date, used_mb, and row_count columns — can you run a record quality audit, string normalisation, and date-by-category pivot on them using used_mb as the field?"},{"title":"Dataset group storage pivot report","prompt":"Can you pivot my storage data by date and dataset_group category and also flag any quality or string issues? I'll send you up to 200 rows with fields: row_id, dataset_group (like 'group-a' or 'group-b'), date, used_mb, and row_count."},{"title":"Row count anomaly and normalisation audit","prompt":"Please audit the quality and normalise string fields in my storage records and give me a date-by-category breakdown — I want to use row_count as the analysis field across my dataset groups."}],"resultDescription":"Three independent analytical reports returned together: (1) a record quality audit highlighting missing, null, or anomalous values in the supplied rows; (2) a string normalisation report on text fields such as dataset_group and row_id; and (3) a date-by-category pivot summarising the selected numeric field (used_mb or row_count) broken down by date and dataset_group. All reports are computed locally with no external data lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, used_mb, or row_count) — validation error","Invalid date format (not YYYY-MM-DD) — schema pattern validation failure","field parameter set to a value other than 'used_mb' or 'row_count' — enum validation error","Payment of 0.01 USDC not completed — request blocked by x402 payment requirement"],"whenToPreferThis":"Choose this endpoint when you need three simultaneous analytical passes — quality auditing, string normalisation, and categorical date pivoting — on a batch of up to 200 dataset storage records in a single $0.01 call. It is best suited for data engineering pipelines that need quick, offline assessment of storage metrics (used_mb, row_count) without any external enrichment or live data lookups. Prefer alternatives if you need more than 200 rows, real-time data, or analytics on fields beyond used_mb and row_count.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T17:26:33.162Z","isFirstParty":false,"canonicalSlug":"dataset-storage-analytics-record-quality-audit-string-normalisation-92cbd94b"}