{"uid":"cap_V3FO-Z7InUmljS5M8WZCS","slug":"label-review-analytics-adjacent-row-changes-date-by-category-pivot-0ff16dae","name":"Label Review Analytics: Adjacent Row Changes + Date by Category Pivot","description":"Label review — Adjacent row changes + Date by category pivot: Adjacent row changes + 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-12-160?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_IFaxyw5GDdueNz1NRILmW","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 analytical reports on label review data: row-over-row change detection and a date-by-category pivot, for up to 200 supplied rows.","exampleAgentPrompt":"Can you run adjacent row change analysis and a date-by-category pivot on my label review data — I have up to 50 rows with fields for row_id, dataset_group, date, reviewed_records, and changed_labels — and I want the analysis focused on the reviewed_records field?","exampleUseCases":[{"title":"ML labeling session trend analysis","prompt":"I have 80 rows of label review logs with columns row_id, dataset_group, date, reviewed_records, and changed_labels — can you compute the row-over-row changes and also pivot them by date and category so I can see which groups had the most activity each day? Focus the analysis on changed_labels."},{"title":"Data quality audit for label pipelines","prompt":"Run a two-part analysis on my label review records: first show me how reviewed_records change between adjacent rows, and then give me a pivot table grouped by date and dataset_group. I have about 120 rows to send."},{"title":"Daily review throughput breakdown by team","prompt":"I want to understand our daily label review throughput across teams — can you take these 60 review records with date, dataset_group, reviewed_records, and changed_labels, and give me both a sequential row-change report and a date-by-category pivot for reviewed_records?"}],"resultDescription":"Two analytical reports: (1) an adjacent row changes report showing sequential deltas for the selected metric (reviewed_records or changed_labels) across the supplied rows, and (2) a date-by-category pivot table aggregating the selected metric by date and dataset_group. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation failure","Invalid date format (not YYYY-MM-DD) causes schema rejection","Null numeric values may affect delta and aggregation calculations","field enum value must be exactly 'reviewed_records' or 'changed_labels'"],"whenToPreferThis":"Choose this endpoint when you need both sequential row-change analysis and a date-by-category pivot on label review data in a single low-cost call. It is purpose-built for label review datasets (reviewed_records, changed_labels) and handles up to 200 rows with two independent analytical views for $0.01. Prefer it over general analytics tools when you want fast, offline computation on your own supplied data without any external API calls or ML predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:14.523Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-adjacent-row-changes-date-by-category-pivot-0ff16dae"}