{"uid":"cap_vwkR8zp1Zk39NgLryvLIR","slug":"label-review-analytics-adjacent-row-changes-iqr-outlier-flags-fcca6e38","name":"Label Review Analytics: Adjacent Row Changes + IQR Outlier Flags","description":"Label review — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags 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-12-157?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_22f3_Vs4HR6i0UDqRBXI8","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 time-series data to detect adjacent row-to-row changes and flag IQR-based outliers across reviewed_records or changed_labels fields, returning two independent analytical reports.","exampleAgentPrompt":"Can you run adjacent row change detection and IQR outlier flagging on my label review data — I have about 50 rows with dates, dataset groups, reviewed_records, and changed_labels, and I want the analysis on the reviewed_records field?","exampleUseCases":[{"title":"Audit annotation pipeline for data drift","prompt":"I have 120 rows of daily label review logs across two dataset groups with columns for date, reviewed_records, and changed_labels — can you run the adjacent row change and IQR outlier analysis on changed_labels to spot any suspicious spikes in our annotation pipeline?"},{"title":"Quality check before ML model retraining","prompt":"Before we kick off retraining, I need to check our last 90 days of label review records for anomalies — please run the IQR outlier flags and adjacent row change report on the reviewed_records field so I can sanity-check the training data quality."},{"title":"Investigate sudden drop in review throughput","prompt":"Our label review throughput looked weird last month — I've got the daily records with row IDs, dates, and dataset groups, can you analyze the reviewed_records field for adjacent row changes and IQR outliers to pinpoint when things went off the rails?"}],"resultDescription":"Returns two independent analytical reports: (1) an adjacent row change report showing delta values between consecutive rows for the selected field, and (2) an IQR outlier flag report identifying which rows fall outside the interquartile range threshold — both scoped to the supplied dataset of up to 200 rows, with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, reviewed_records, or changed_labels) — validation error returned","Date fields not matching YYYY-MM-DD pattern — schema validation failure","Invalid field selector value (must be 'reviewed_records' or 'changed_labels') — enum validation error","All-null numeric values for the selected field — analysis may produce empty or degenerate output","Payment of 0.01 USDC not completed — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis on label review tabular data — specifically adjacent row change detection and IQR-based outlier flagging — without any external API calls, ML predictions, or professional assessments. Ideal for MLOps and data quality workflows where you want two fast, reproducible analytical reports in a single $0.01 call on up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:23.272Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-adjacent-row-changes-iqr-outlier-flags-fcca6e38"}