{"uid":"cap_0Aln2RlNG008o1Jz6QfuZ","slug":"label-review-trailing-mean-series-adjacent-row-changes-c97cdab3","name":"Label Review — Trailing Mean Series + Adjacent Row Changes","description":"Label review — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes 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 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two analytical reports (trailing mean series and adjacent row changes) on label review data (reviewed_records or changed_labels) for up to 200 rows.","exampleAgentPrompt":"Run trailing mean and adjacent row change reports on my label review data — here are 4 rows with dates 2026-10-01 through 2026-10-04, dataset groups group-a and group-b, reviewed_records values 10, 20, 15, 30 and changed_labels 1, 2, 3, 4 — analyze the reviewed_records field with a rolling window of 3.","exampleUseCases":[{"title":"ML label QA trend monitoring","prompt":"I have a week of daily label review records with reviewed_records and changed_labels counts per dataset group — can you run trailing mean (window=5) and adjacent row change reports on the changed_labels field so I can see where label churn is spiking?"},{"title":"Rolling average for annotation throughput","prompt":"Here are 50 rows of annotation review data by date and group — compute the trailing mean series with a window of 7 and also show me the row-over-row differences for reviewed_records so I can track throughput trends."},{"title":"Dataset group comparison over time","prompt":"I want to compare how reviewed_records evolved across two dataset groups last month — run the trailing mean and adjacent row change analysis with a window of 4 on these 120 rows of label review data."}],"resultDescription":"Two independent analytical reports computed locally on the supplied data: (1) a trailing mean series showing the rolling average of the selected field (reviewed_records or changed_labels) over the specified window, and (2) an adjacent row changes report showing the difference between each consecutive row's value for the selected field — both keyed by row_id and date.","failureModes":["Missing required fields (row_id, date, reviewed_records, changed_labels) causes validation error","Date strings not matching YYYY-MM-DD pattern are rejected","More than 200 rows submitted exceeds the maxItems limit","Window value outside 2–200 range is invalid","Null numeric values may affect mean/diff calculations where non-null data is expected","Invalid field enum value (not 'reviewed_records' or 'changed_labels') causes rejection"],"whenToPreferThis":"Choose this endpoint when you have label review time-series data and need both a rolling mean trend and sequential row-to-row difference analysis in a single cheap call ($0.01 USDC for both reports). Ideal for ML ops, data annotation pipelines, or QA workflows where you want to detect spikes or trends in review volume or label change rates without sending data to an external prediction or live-lookup service.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:36:25.855Z","isFirstParty":false,"canonicalSlug":"label-review-trailing-mean-series-adjacent-row-changes-c97cdab3"}