{"uid":"cap_k5ecsNMjHkKLIXZDtFdJo","slug":"join-coverage-iqr-outlier-flags-date-by-category-pivot-a21822f3","name":"Join Coverage IQR Outlier Flags + Date-by-Category Pivot","description":"Join coverage — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied join coverage records (matched_rows, source_rows). 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-09-178?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"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","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"}}},"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_PCyz3WjSYayMzIqpGItTM","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 join coverage data: IQR-based outlier flagging and a date-by-category pivot, for up to 200 rows of matched_rows/source_rows records.","exampleAgentPrompt":"Can you flag IQR outliers and build a date-by-category pivot for my join coverage data? I have 4 rows with matched_rows and source_rows across two groups — group-a and group-b — dated 2026-10-01 through 2026-10-04, and I want to analyze the matched_rows field.","exampleUseCases":[{"title":"Outlier detection in ETL join coverage","prompt":"I have daily join coverage records for two pipeline groups, group-a and group-b, covering October 2026. Can you flag any IQR outliers in the matched_rows field and also show me a date-by-category pivot so I can see which days and groups look unusual?"},{"title":"Source row coverage audit by dataset group","prompt":"Run an IQR outlier analysis and a date-by-category pivot on my source_rows coverage data — I've got about 50 rows across three dataset groups spanning the last two months. I want to see which groups have statistically anomalous source counts."},{"title":"Join match rate QA before data pipeline release","prompt":"Before we release this pipeline, can you check our join coverage table for outliers using IQR and pivot the matched_rows by date and dataset_group? We have around 120 records and I need both reports at once."}],"resultDescription":"Returns two analytical reports computed locally on the supplied data: (1) IQR outlier flags indicating which rows have matched_rows or source_rows values outside the interquartile range, and (2) a pivot table of the selected field aggregated by date and dataset_group category. No external data is fetched.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — request rejected","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","All values null for selected field — IQR computation may be undefined","Invalid field enum value (must be matched_rows or source_rows) — rejected"],"whenToPreferThis":"Choose this endpoint when you need both IQR outlier detection and a date-by-category pivot on join coverage data (matched_rows, source_rows) in a single low-cost call, with no external lookups or ML models — pure local statistical computation on up to 200 user-supplied rows. Ideal for data engineers and analysts monitoring ETL join quality who need fast, deterministic anomaly flags and summary pivots.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:38.688Z","isFirstParty":false,"canonicalSlug":"join-coverage-iqr-outlier-flags-date-by-category-pivot-a21822f3"}