{"uid":"cap__JQhhY6hh_bca-Mv19-l7","slug":"asset-revisions-string-normalisation-date-by-category-pivot-a59c5267","name":"Asset Revisions — String Normalisation + Date by Category Pivot","description":"Asset revisions — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied asset revisions records (revision_count, rework_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-creative-14-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","revision_count","rework_hours"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"rework_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"revision_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","rework_hours":1,"revision_count":10},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","rework_hours":2,"revision_count":20},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","rework_hours":3,"revision_count":15},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","rework_hours":4,"revision_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["revision_count","rework_hours"],"type":"string","default":"revision_count"}}},"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_c4YKwexx55LjLf721CVjQ","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":"Applies string normalisation and date-by-category pivot analysis to up to 200 rows of asset revision records (revision_count, rework_hours), producing 2 independent analytical reports.","exampleAgentPrompt":"Can you run a string normalisation and date-by-category pivot on these asset revision records — I have about 50 rows with row_id, asset_group, date, revision_count, and rework_hours fields — and give me both reports for the revision_count field?","exampleUseCases":[{"title":"Pivot revision counts by asset group","prompt":"I have 120 rows of asset revision logs with dates, asset group names, and revision counts. Can you normalize the string fields and pivot the revision_count by category and date so I can see trends across groups?"},{"title":"Analyse rework hours across categories","prompt":"Run a string normalisation and date-pivot analysis on my rework_hours data — I've got 80 asset revision records grouped into four asset groups with dates ranging over the last quarter. Use rework_hours as the target field."},{"title":"Clean and summarise revision data for reporting","prompt":"I need two reports from my asset revision dataset — one normalising the text fields and one pivoting revision counts by asset group and date. There are about 200 rows. Can you process them and give me the results?"}],"resultDescription":"Returns 2 independent analytical reports: (1) a string normalisation report cleaning and standardising text fields in the supplied asset revision rows, and (2) a date-by-category pivot table aggregating the selected numeric field (revision_count or rework_hours) across asset_group and date dimensions. No external data is fetched; all processing is done on the supplied rows.","failureModes":["Rows exceed 200-item limit — request rejected","Missing required fields (row_id, asset_group, date, revision_count, or rework_hours) cause validation errors","Malformed date strings not matching YYYY-MM-DD pattern cause schema rejection","Numeric values outside allowed range trigger validation failure","Empty rows array (minItems:1 not met) causes error","Invalid field enum value (not revision_count or rework_hours) causes rejection"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of asset revision data and need both string normalisation and a date-by-category pivot in a single $0.01 call. It bundles two reports at a fixed low price, making it efficient for batch offline analysis of revision_count or rework_hours without requiring live lookups or external data sources.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:31:56.345Z","isFirstParty":false,"canonicalSlug":"asset-revisions-string-normalisation-date-by-category-pivot-a59c5267"}