{"uid":"cap_YQO1I2OidOjvPirT4NLQM","slug":"label-review-analytics-iqr-outlier-flags-date-by-category-pivot-325d9c69","name":"Label Review Analytics: IQR Outlier Flags + Date-by-Category Pivot","description":"Label review — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied label review records (reviewed_records, changed_labels). 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-12-178?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","reviewed_records","changed_labels"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"changed_labels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"reviewed_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","changed_labels":1,"reviewed_records":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","changed_labels":2,"reviewed_records":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","changed_labels":3,"reviewed_records":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","changed_labels":4,"reviewed_records":30}],"maxItems":200,"minItems":1},"field":{"enum":["reviewed_records","changed_labels"],"type":"string","default":"reviewed_records"}}},"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_e_5wriR6Yk3kJetxWTo4F","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":"Analyzes label review records to detect IQR-based outliers and produce a date-by-category pivot table, returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run IQR outlier detection and a date-by-category pivot on my label review data? I have rows with row_id, dataset_group, date, reviewed_records, and changed_labels — flag outliers on the reviewed_records field.","exampleUseCases":[{"title":"Annotation QA spike detection","prompt":"I have 150 rows of label review logs with row_id, dataset_group, date, reviewed_records, and changed_labels — can you flag any IQR outliers in changed_labels and also give me a pivot of activity by date and group?"},{"title":"ML pipeline audit by team","prompt":"We ran a week of labeling across three dataset groups and I want to know which days or groups had abnormally high reviewed_records counts — run IQR outlier flags and a date-by-category breakdown on the data I'm sending you."},{"title":"Label quality review summary","prompt":"Here's my label review export with up to 200 records across multiple dataset groups — please detect outliers using IQR on the reviewed_records field and generate a pivot table showing totals by date and category."}],"resultDescription":"Returns two independent reports: (1) an IQR outlier flag report identifying which rows have values outside the interquartile range for the selected field (reviewed_records or changed_labels), and (2) a date-by-category pivot table summarizing the selected metric grouped by date and dataset_group. All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Input array is empty or contains more than 200 rows — request rejected","Missing required fields (row_id, date, dataset_group, reviewed_records, changed_labels) on any row — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","All values for the selected field are null — IQR computation not possible","Field selector not one of 'reviewed_records' or 'changed_labels' — enum validation error"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier detection and a date-by-category pivot on label review records in a single low-cost call. It is purpose-built for annotation QA workflows where you have up to 200 rows with reviewed_records and changed_labels metrics segmented by dataset group and date. Prefer it over generic analytics tools when you want two bundled reports at $0.01 USDC without any external lookups or live data dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:49.841Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-iqr-outlier-flags-date-by-category-pivot-325d9c69"}