{"uid":"cap_hai-Xwu0Q-T8fMszefDaw","slug":"label-review-analytics-numeric-distribution-date-by-category-pivot-d1897f6c","name":"Label Review Analytics — Numeric Distribution & Date-by-Category Pivot","description":"Label review — Numeric distribution + Date by category pivot: Numeric distribution + 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-12-083?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_jq8A2QCCxLMsuarHw5wm3","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 label review data: a numeric distribution of reviewed_records or changed_labels, and a date-by-category pivot grouped by dataset_group.","exampleAgentPrompt":"Can you analyze my label review data — I have up to 200 rows with row_id, dataset_group, date, reviewed_records, and changed_labels — and give me both a numeric distribution of reviewed_records and a date-by-category pivot grouped by dataset_group?","exampleUseCases":[{"title":"Annotation quality audit by team","prompt":"I have 150 rows of label review records from three dataset groups — 'team-alpha', 'team-beta', and 'team-gamma' — with dates and counts of reviewed_records and changed_labels. Can you give me a numeric distribution of changed_labels and a date-by-category pivot so I can see which team is changing the most labels over time?"},{"title":"ML pipeline label drift detection","prompt":"I need to understand how my reviewed_records are distributed and how they trend by category and date across my label review batches. Here are my 80 rows with row_id, dataset_group, date, reviewed_records, and changed_labels — run the numeric distribution and date pivot reports on reviewed_records for me."},{"title":"Monthly label review summary report","prompt":"Can you summarize last month's label review data for me? I'll provide about 60 rows — each has a row_id, a dataset_group like 'images' or 'text', a date, and counts for reviewed_records and changed_labels. I want a distribution of reviewed_records and a pivot of those counts broken down by dataset_group and date."}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution (histogram/summary stats) for the chosen field (reviewed_records or changed_labels) across all supplied rows, and (2) a date-by-category pivot table showing values aggregated by date and dataset_group — all computed locally from the supplied data, no external lookups.","failureModes":["Fewer than 1 or more than 200 rows supplied — request rejected","Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) on any row — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) — schema rejection","Invalid field enum value (must be 'reviewed_records' or 'changed_labels') — error response","Payment of 0.01 USDC not fulfilled — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you have label review data (reviewed_records and changed_labels) organized by dataset_group and date, and need both a numeric distribution and a date-by-category pivot in a single low-cost call. Ideal for ML annotation pipelines, data labeling quality audits, or any workflow needing offline analytical summaries without external data access. At $0.01 USDC for two reports on up to 200 rows, it is cost-effective for batch analytical summarization of annotation metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:53.865Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-numeric-distribution-date-by-category-pivot-d1897f6c"}