{"uid":"cap_eq5TCwuJmTryd48sSa_EK","slug":"dataset-storage-analytics-record-quality-audit-string-normalisation-5233e2ad","name":"Dataset Storage Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags","description":"Dataset storage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + 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-18-203?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_I8g2ZM-eMvwOyuuN8Sjt3","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, string normalisation, and IQR-based outlier flagging — up to 200 rows per call.","exampleAgentPrompt":"Can you audit the quality of these dataset storage records, normalize the group name strings, and flag any IQR outliers in the used_mb field? I have 4 rows: row-1 on 2026-10-01 group-a used_mb=10 row_count=1, row-2 on 2026-10-02 group-b used_mb=20 row_count=2, row-3 on 2026-10-03 group-a used_mb=15 row_count=3, row-4 on 2026-10-04 group-b used_mb=30 row_count=4.","exampleUseCases":[{"title":"Storage usage anomaly detection","prompt":"I have 50 rows of daily dataset storage records with 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":"Pre-pipeline data quality gate","prompt":"Before I load these 120 dataset storage records into our pipeline, can you run a full quality audit on them and normalize the dataset_group strings so the labels are consistent?"},{"title":"Monthly storage dataset review","prompt":"Run a record quality audit plus IQR outlier check on row_count for these dataset storage entries covering October 2026 — I want to catch any bad rows before the monthly report goes out."}],"resultDescription":"Returns three independent analytical reports for up to 200 supplied dataset storage rows: (1) a record quality audit highlighting missing values, type mismatches, and row-level quality issues; (2) string normalisation results for text fields such as dataset_group; and (3) IQR-based outlier flags identifying statistically anomalous values in either used_mb or row_count depending on the selected field.","failureModes":["Exceeding 200 rows causes a validation error — batch must be split","Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause schema rejection","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric values outside allowed range (-1000000000000 to 1000000000000) are rejected","Too few rows may produce unreliable IQR outlier detection (IQR needs sufficient spread)","Payment failure via x402 prevents execution"],"whenToPreferThis":"Choose this endpoint when you need three combined data quality operations — quality audit, string normalisation, and IQR outlier detection — on dataset storage records in a single low-cost call. It is ideal for pre-pipeline data validation, anomaly detection on storage metrics, or cleaning batch exports of up to 200 rows without requiring external lookups, live data, or professional data services.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:48.843Z","isFirstParty":false,"canonicalSlug":"dataset-storage-analytics-record-quality-audit-string-normalisation-5233e2ad"}