{"uid":"cap_SIEzWmK5Xl5CyQBOFAkWa","slug":"dataset-storage-analytics-record-quality-audit-exact-row-bafdce68","name":"Dataset Storage Analytics: Record Quality Audit + Exact Row Deduplication + IQR Outlier Flags","description":"Dataset storage — Record quality audit + Exact row deduplication + IQR outlier flags: Record quality audit + Exact row deduplication + IQR outlier flags 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-18-220?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_CEpGOsbv8YvCB3_ZI86fk","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 (used_mb, row_count): a record quality audit, exact row deduplication check, and IQR-based outlier flagging — all from supplied data, no external lookups.","exampleAgentPrompt":"Can you audit my dataset storage records for quality issues, exact duplicates, and IQR outliers? Here are up to 200 rows with fields row_id, dataset_group, date, used_mb, and row_count — please analyze the used_mb field.","exampleUseCases":[{"title":"Storage usage anomaly detection","prompt":"I have a list of up to 150 dataset storage records with row IDs, group names, dates, used_mb, and row_count values. Can you flag any IQR outliers in the used_mb field and tell me which rows look suspicious?"},{"title":"Duplicate row audit before ETL pipeline","prompt":"Before I run my ETL pipeline, can you check these 80 dataset storage records for exact duplicate rows and give me a deduplication report? The records have row_id, dataset_group, date, used_mb, and row_count columns."},{"title":"Full data quality check on row_count metrics","prompt":"Run a full quality audit on my dataset storage records — I want quality issues, duplicates, and outlier flags all in one go, focused on the row_count field. I have about 60 rows with dataset_group, date, used_mb, and row_count."}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit identifying missing, malformed, or invalid fields; (2) an exact row deduplication report listing identical rows; and (3) IQR-based outlier flags for the selected numeric field (used_mb or row_count). All analysis is performed locally on the supplied data with no external data access.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, used_mb, or row_count) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","Numeric values outside allowed range trigger schema errors","Empty rows array returns an error","Payment not included or incorrect amount causes 402 rejection"],"whenToPreferThis":"Choose this endpoint when you need a combined, low-cost ($0.01 USDC) batch analysis of dataset storage records covering quality, deduplication, and outlier detection in a single call — especially for datasets up to 200 rows where no live data lookup or external enrichment is needed. It is ideal for pre-pipeline data validation, storage metric audits, and lightweight data hygiene checks on used_mb or row_count fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:44:06.224Z","isFirstParty":false,"canonicalSlug":"dataset-storage-analytics-record-quality-audit-exact-row-bafdce68"}