{"uid":"cap_hisMsfMyKBeyVjFEJ2RwB","slug":"dataset-storage-iqr-outlier-flagging-and-string-normalisation-d939d43b","name":"Dataset Storage IQR Outlier Flagging and String Normalisation","description":"Dataset storage — String normalisation + IQR outlier flags: String normalisation + 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-18-032?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_g5i0rsJGUy74XiAgk28CC","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":"Normalises string fields and flags IQR-based outliers in dataset storage records (used_mb, row_count) for up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Can you run IQR outlier detection and string normalisation on my dataset storage records — I have about 50 rows with fields like row_id, dataset_group, date, used_mb, and row_count, and I want to flag the outliers in used_mb?","exampleUseCases":[{"title":"Storage spike detection in data pipeline","prompt":"I have 120 rows of daily dataset storage records with columns row_id, dataset_group, date, used_mb, and row_count — can you normalise the group names and flag any rows where used_mb is an IQR outlier?"},{"title":"Row count anomaly audit across groups","prompt":"Audit my storage dataset for anomalous row_count values — here are 80 records with date, row_id, dataset_group, used_mb, and row_count. Flag anything that falls outside 1.5x the interquartile range and also normalise the string fields."},{"title":"Monthly storage quality check","prompt":"Each month I collect dataset storage snapshots — can you take these 200 rows and produce two reports: one with normalised dataset_group strings and one flagging outlier used_mb values using IQR?"}],"resultDescription":"Two independent analytical reports per call: one covering string normalisation of dataset_group and related string fields, and one flagging rows whose selected numeric field (used_mb or row_count) falls beyond 1.5× the interquartile range. Results are computed locally with no external lookup, covering up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","All numeric values identical — IQR is zero, no outliers flagged","Null values in selected numeric field — may reduce outlier detection accuracy"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and IQR-based outlier flagging on dataset storage records (used_mb, row_count) in a single low-cost call. It is best suited for batch quality checks on up to 200 rows of storage metrics without requiring external data, live predictions, or professional assessments. Prefer alternatives if you need correlation analysis, trend fitting, or comparisons against benchmark datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:02.531Z","isFirstParty":false,"canonicalSlug":"dataset-storage-iqr-outlier-flagging-and-string-normalisation-d939d43b"}