{"uid":"cap_zMI6cokZShfvlFKYL81y3","slug":"dataset-storage-analytics-numeric-distribution-iqr-outlier-flags-5f0e50af","name":"Dataset Storage Analytics — Numeric Distribution & IQR Outlier Flags","description":"Dataset storage — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags for supplied dataset storage records (used_mb, row_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-080?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__953Lfu_AzX-zU8egwBM8","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":"Computes numeric distribution statistics and IQR-based outlier flags for used_mb and row_count fields in dataset storage records (up to 200 rows, 2 reports per call).","exampleAgentPrompt":"Can you analyze the numeric distribution of used_mb for my dataset storage records and flag any IQR outliers? Here are my rows: group-a had 10 MB on 2026-10-01, 15 MB on 2026-10-03; group-b had 20 MB on 2026-10-02 and 30 MB on 2026-10-04.","exampleUseCases":[{"title":"Storage anomaly detection in ETL pipeline","prompt":"I have 150 daily dataset storage records with used_mb and row_count values — can you run IQR outlier detection on used_mb to find which days had abnormally large storage usage?"},{"title":"Distribution audit before data migration","prompt":"Before we migrate our datasets, can you compute the numeric distribution statistics for row_count across all our storage records so we know the spread, quartiles, and any outliers?"},{"title":"Ongoing storage health check for multiple groups","prompt":"We track storage under two dataset groups, 'warehouse' and 'archive'. Can you flag any IQR outliers in used_mb across both groups using our last 90 days of records?"}],"resultDescription":"Returns two analytical reports (one per selected field or both used_mb and row_count): numeric distribution statistics (min, max, mean, median, quartiles) and IQR-based outlier flags identifying which rows fall outside the expected range. Each report covers up to 200 input rows with no external data lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, dataset_group, date, used_mb, or row_count) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (±1 trillion) — rejected","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Use this endpoint when you need fast, local statistical analysis (distribution + IQR outlier detection) on dataset storage records (used_mb, row_count) without any external lookups or predictions. Ideal for data quality audits, anomaly detection in storage metrics, and pre-migration dataset reviews. Best for batch sizes up to 200 rows where two analytical reports are needed affordably at $0.01 USDC per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:50.945Z","isFirstParty":false,"canonicalSlug":"dataset-storage-analytics-numeric-distribution-iqr-outlier-flags-5f0e50af"}