{"uid":"cap_CCYHt0At-w4zTag5w-rPL","slug":"label-review-adjacent-row-changes-explicit-missing-value-fill-c00e2b8e","name":"Label Review — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Label review — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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-12-162?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_0NF_JZZp2jodTWlnxcBbO","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 by computing adjacent row changes and filling missing values explicitly, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run adjacent row change detection and missing-value fill (using median) on my label review data? I have 4 rows across group-a and group-b from 2026-10-01 to 2026-10-04, with reviewed_records and changed_labels — I want to target the reviewed_records field.","exampleUseCases":[{"title":"Spot annotation count jumps across dates","prompt":"I have daily label review records for two dataset groups across October — can you check for adjacent row changes in changed_labels and also fill any missing values using the median? Here are up to 50 rows with row_id, date, dataset_group, reviewed_records, and changed_labels."},{"title":"Impute missing reviewed_records with zero fill","prompt":"Some of my label review rows are missing reviewed_records values. Can you fill those in using zero and also show me adjacent row changes for the reviewed_records field? I'll send you about 120 rows across multiple dataset groups."},{"title":"QA label review pipeline before model training","prompt":"Before we retrain, I want to audit our label review metrics — can you run both the adjacent row change report and the explicit missing-value fill using mean on changed_labels for our 80-row review dataset grouped by dataset_group and date?"}],"resultDescription":"Returns two analytical reports: (1) an adjacent row changes report showing deltas between consecutive rows for the specified field, and (2) an explicit missing-value fill report where nulls in the target field are replaced using the chosen method (zero, mean, or median). Both reports operate on the supplied rows only — no external data or predictions are involved.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 bounds are rejected","Empty rows array (minItems: 1) returns an error","Invalid enum values for field or method parameters cause schema validation failure"],"whenToPreferThis":"Choose this endpoint when you need two specific analytical operations on label review tabular data simultaneously — adjacent row change detection and explicit missing-value imputation — without any external lookup, model prediction, or live data fetching. It is best suited for ML data pipeline QA, annotation audits, and preprocessing steps where you have up to 200 rows and want fast, deterministic local computation at minimal cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:10.978Z","isFirstParty":false,"canonicalSlug":"label-review-adjacent-row-changes-explicit-missing-value-fill-c00e2b8e"}