{"uid":"cap_hJoZ7V1qsL7L329mGP_4K","slug":"label-review-analytics-category-frequencies-adjacent-row-changes-c10c79f9","name":"Label Review Analytics: Category Frequencies + Adjacent Row Changes","description":"Label review — Category frequencies + Adjacent row changes: Category frequencies + 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 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-12-104?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_ulLg6-iaTvoPfb0yss6Iv","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":"Computes category frequency distribution and adjacent-row delta analysis for label review records (reviewed_records or changed_labels) across up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Analyze my label review data for the last week — I have rows with dataset_group, date, reviewed_records, and changed_labels fields — and give me both a category frequency breakdown by group and the adjacent row changes for the reviewed_records field. Here are the rows: [{\"row_id\":\"r1\",\"date\":\"2024-11-01\",\"dataset_group\":\"images\",\"reviewed_records\":120,\"changed_labels\":15},{\"row_id\":\"r2\",\"date\":\"2024-11-02\",\"dataset_group\":\"text\",\"reviewed_records\":95,\"changed_labels\":8},{\"row_id\":\"r3\",\"date\":\"2024-11-03\",\"dataset_group\":\"images\",\"reviewed_records\":140,\"changed_labels\":22}].","exampleUseCases":[{"title":"ML annotation pipeline audit","prompt":"I have 50 rows of label review records from our annotation team — each row has a row_id, date, dataset_group, reviewed_records count, and changed_labels count. Can you run the category frequency and adjacent row change reports on the reviewed_records field so I can see which dataset groups dominate and how daily review volume shifts?"},{"title":"Data quality drift detection","prompt":"Our labeling QA team flagged potential drift in label corrections over the past month. I'll give you up to 150 rows with date, dataset_group, reviewed_records, and changed_labels — please analyze the changed_labels field to get category frequencies by group and the adjacent row deltas so we can spot sudden spikes in label changes."},{"title":"Weekly review summary by category","prompt":"Give me a quick summary of this week's label review session: here are 80 rows with dataset groups like 'medical-images' and 'audio-clips', each with reviewed_records and changed_labels counts. Run both the category frequencies and adjacent row changes reports on reviewed_records and tell me which groups had the most activity and where the biggest day-over-day jumps occurred."}],"resultDescription":"Returns two independent analytical reports: (1) category frequencies showing how often each dataset_group value appears across the supplied rows, and (2) adjacent row changes showing the sequential delta of the selected metric field (reviewed_records or changed_labels) between consecutive rows. Both reports are computed locally with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required row fields (row_id, dataset_group, date, reviewed_records, changed_labels) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric fields outside allowed range (-1T to 1T) — rejected","Empty rows array — minItems:1 violated","Invalid field enum value (must be 'reviewed_records' or 'changed_labels') — error"],"whenToPreferThis":"Choose this endpoint when you have structured label review tabular data (up to 200 rows) and need both a category frequency distribution across dataset groups AND sequential adjacent-row change analysis in a single call for a flat $0.01 USDC fee. It is ideal for ML/annotation pipelines auditing label correction rates or review throughput over time. Prefer alternatives if you need live data lookup, predictive modeling, more than 200 rows, or only one of the two reports.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:18.963Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-category-frequencies-adjacent-row-changes-c10c79f9"}