{"uid":"cap_WWqAA3xtlq0lUHTDUi4YN","slug":"dataset-storage-analytics-record-quality-audit-string-normalisation-58ac7f99","name":"Dataset Storage Analytics: Record Quality Audit + String Normalisation + Category Frequencies","description":"Dataset storage — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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-18-195?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_fyxrl6Fm55phX388iUYm6","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 dataset storage records — record quality audit, string normalisation, and category frequency analysis — for up to 200 rows of used_mb and row_count data.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and category frequency analysis on these dataset storage records I have — they include row IDs, dataset groups like 'group-a' and 'group-b', dates, used_mb values, and row counts for about 50 rows?","exampleUseCases":[{"title":"Storage usage data quality check","prompt":"I have 120 rows of storage usage data with fields like row_id, dataset_group, date, used_mb, and row_count — can you audit them for quality issues like missing values or out-of-range numbers, normalise the string fields, and tell me how often each dataset_group appears?"},{"title":"Monthly dataset group frequency report","prompt":"Here are our monthly storage records across dataset groups like 'prod', 'staging', and 'archive' — run category frequency analysis plus a quality audit and string normalisation on all 80 rows so I can see which groups dominate usage."},{"title":"Pre-migration data cleansing check","prompt":"Before we migrate these 200 dataset storage records to the new warehouse, can you do a record quality audit to flag bad data, normalise the dataset_group strings, and give me a frequency breakdown by category?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging nulls, invalid values, and anomalies in used_mb and row_count fields; (2) a string normalisation report cleaning and standardising text fields like dataset_group; (3) a category frequency table showing counts and distributions across dataset_group values. All derived solely from the supplied rows — no external lookups or live data.","failureModes":["Exceeds 200-row limit — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error","Invalid date format (must be YYYY-MM-DD) — parsing error","Numeric fields outside allowed range (±1,000,000,000,000) — validation rejection","Empty rows array — error response","Payment not included or insufficient — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need a combined, low-cost batch analysis of dataset storage records covering data quality, string cleanliness, and categorical distribution in one call. It is best suited for offline or near-real-time data auditing workflows where you already hold the row data locally and need structured analytical reports without any external API calls, predictions, or professional assessments.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:08.853Z","isFirstParty":false,"canonicalSlug":"dataset-storage-analytics-record-quality-audit-string-normalisation-58ac7f99"}