{"uid":"cap_E3yVGddBQUXwprnbP3dSm","slug":"editorial-turnaround-string-normalisation-adjacent-row-changes-e2150b6d","name":"Editorial Turnaround — String Normalisation + Adjacent Row Changes","description":"Editorial turnaround — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied editorial turnaround records (draft_hours, review_hours). 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-news-15-029?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","draft_hours","review_hours"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"draft_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"review_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"channel_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","draft_hours":10,"review_hours":1,"channel_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","draft_hours":20,"review_hours":2,"channel_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","draft_hours":15,"review_hours":3,"channel_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","draft_hours":30,"review_hours":4,"channel_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["draft_hours","review_hours"],"type":"string","default":"draft_hours"}}},"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_SxUGFeCGrgIw0hBHnJNKK","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 editorial turnaround records (draft_hours, review_hours) to produce string normalization and adjacent row change reports for up to 200 rows.","exampleAgentPrompt":"Can you run string normalization and adjacent row change analysis on my editorial turnaround data? I have 4 rows with draft_hours and review_hours for channel groups group-a and group-b from October 2026 — analyze the draft_hours field.","exampleUseCases":[{"title":"Newsroom draft time consistency audit","prompt":"I have a set of editorial records from October 2026 with draft hours logged per channel group — can you normalize the string values and flag where adjacent rows differ significantly in draft_hours? I have about 50 rows across group-a and group-b."},{"title":"Review cycle adjacent-change tracking","prompt":"Run the string normalization and adjacent row changes reports on my review_hours data — I've got 120 editorial turnaround rows spanning multiple channel groups and dates in 2026 and I want to see where consecutive entries shift."},{"title":"Editorial workflow data cleanup","prompt":"I need to clean up and compare my editorial turnaround records — can you apply string normalization and detect adjacent row changes for draft_hours? I have around 30 rows with dates in Q4 2026, each tagged to a channel group."}],"resultDescription":"Returns two analytical reports: (1) a string normalization report standardizing text fields across the supplied editorial turnaround rows, and (2) an adjacent row changes report highlighting differences between consecutive rows for the selected metric (draft_hours or review_hours). Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, channel_group, date, draft_hours, review_hours) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Invalid field enum value (not draft_hours or review_hours) causes rejection","Empty rows array (minItems: 1 violated) returns error","Payment not included or insufficient USDC causes 402 response"],"whenToPreferThis":"Choose this endpoint when you need two specific local analytical operations — string normalization and adjacent row change detection — applied to editorial turnaround data (draft_hours, review_hours) without any live lookups or external data enrichment. It is ideal for batch processing up to 200 editorial records in a single cheap call ($0.01 USDC) where the data is self-contained and no prediction or professional assessment is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:36:53.620Z","isFirstParty":false,"canonicalSlug":"editorial-turnaround-string-normalisation-adjacent-row-changes-e2150b6d"}