{"uid":"cap_6X1Hv5i3ZLDyN7jcoYUlT","slug":"copyediting-throughput-string-normalisation-date-by-category-pivot-1837fe55","name":"Copyediting Throughput — String Normalisation + Date by Category Pivot","description":"Copyediting throughput — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied copyediting throughput records (edited_words, editing_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-writing-13-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","document_group","date","edited_words","editing_hours"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"edited_words":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"editing_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"document_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","edited_words":10,"editing_hours":1,"document_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","edited_words":20,"editing_hours":2,"document_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","edited_words":15,"editing_hours":3,"document_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","edited_words":30,"editing_hours":4,"document_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["edited_words","editing_hours"],"type":"string","default":"edited_words"}}},"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_jk1AizI-AyIQAC2Wk5zZ2","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":"Runs two local analytical reports on copyediting throughput data: string normalisation and a date-by-category pivot, for up to 200 rows of edited_words/editing_hours records.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on my copyediting throughput records? I have 4 rows with document groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and I want to analyse the edited_words field.","exampleUseCases":[{"title":"Editorial team weekly throughput review","prompt":"I have weekly copyediting records for two document groups — 'manuals' and 'articles' — with dates and edited word counts for each editor session. Can you normalise the strings and pivot the edited_words by document group and date so I can see throughput trends?"},{"title":"Comparing editing hours across document categories","prompt":"I've got 50 rows of copyediting session data with dates, document groups, and editing hours logged. Run the string normalisation and date-by-category pivot on the editing_hours field so I can compare how time is distributed across categories over the past month."},{"title":"Content agency project audit","prompt":"We need to audit our copyediting throughput across client projects — I have up to 150 rows with project group labels, dates, and edited word counts. Please normalise the data and give me a pivot of edited_words by project group and date."}],"resultDescription":"Returns two analytical reports: (1) a string-normalised view of the copyediting throughput records, and (2) a date-by-category pivot table aggregating the selected metric (edited_words or editing_hours) by document_group across dates. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request exceeds row limit","Missing required fields (row_id, document_group, date, edited_words, editing_hours) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","field enum value not 'edited_words' or 'editing_hours' causes rejection","Null numeric values may reduce pivot completeness","Empty rows array rejected by minItems constraint"],"whenToPreferThis":"Choose this endpoint when you need two specific analytical operations — string normalisation and date-by-category pivot — applied simultaneously to copyediting throughput records (edited_words, editing_hours) for up to 200 rows, at a flat $0.01 USDC for both reports. It is a purely local computation with no external data access, making it fast, deterministic, and privacy-safe for internal editorial data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-05T00:40:59.869Z","isFirstParty":false,"canonicalSlug":"copyediting-throughput-string-normalisation-date-by-category-pivot-1837fe55"}