{"uid":"cap_574dzKWodLAsOvpdaDvhv","slug":"dataset-storage-iqr-outlier-flags-date-by-category-pivot-analytics-646f0cd0","name":"Dataset Storage IQR Outlier Flags + Date by Category Pivot Analytics","description":"Dataset storage — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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-18-178?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_E2vCmm8v-MLod1kGsUBBY","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 two local analytical reports on submitted dataset storage records: IQR-based outlier flagging and a date-by-category pivot table, for up to 200 rows of used_mb or row_count data.","exampleAgentPrompt":"Can you run IQR outlier detection and a date-by-category pivot on this storage dataset — field is used_mb — here are my rows: row-1, group-a, 2026-10-01, 10 MB, 1 row; row-2, group-b, 2026-10-02, 20 MB, 2 rows; row-3, group-a, 2026-10-03, 15 MB, 3 rows; row-4, group-b, 2026-10-04, 300 MB, 4 rows?","exampleUseCases":[{"title":"Storage anomaly detection for data ops","prompt":"I have 50 storage records across several dataset groups with dates and used_mb values — can you flag which rows are IQR outliers and also give me a pivot of usage by date and group, using the used_mb field?"},{"title":"Row count spike investigation","prompt":"Something looks off in our pipeline metrics — can you run IQR outlier flagging on our row_count data across these 120 dataset records and also pivot them by date and dataset_group so I can see where spikes cluster?"},{"title":"Monthly storage audit by team group","prompt":"I need to audit storage consumption across our teams for Q4 — here are 180 records with used_mb, dates, and group names. Can you flag the statistical outliers and generate a date-by-category breakdown for me?"}],"resultDescription":"Returns two analytical reports: (1) each input row annotated with an IQR-based outlier flag indicating whether used_mb or row_count is statistically anomalous, and (2) a pivot table summarizing the chosen metric (used_mb or row_count) broken down by date and dataset_group category. No external data is fetched; all computation is local.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, used_mb, row_count) on any row","Date values not matching YYYY-MM-DD format — validation error","Invalid field enum value (must be 'used_mb' or 'row_count')","All values null for the selected metric — IQR computation undefined","Payment not included or insufficient — 402 response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 dataset storage records (with used_mb and row_count fields) and need both IQR outlier detection and a date-by-category pivot in a single low-cost call. It is ideal for offline/batch analytical passes over collected storage metrics without requiring a live database connection or external data enrichment. Prefer this over general-purpose analytics tools when cost predictability ($0.01 flat for both reports) and no-side-effect local computation matter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:45:48.228Z","isFirstParty":false,"canonicalSlug":"dataset-storage-iqr-outlier-flags-date-by-category-pivot-analytics-646f0cd0"}