{"uid":"cap_iC1HLeZnzJaBqhhLq47jw","slug":"render-jobs-string-normalisation-date-by-category-pivot-08f56449","name":"Render Jobs String Normalisation + Date by Category Pivot","description":"Render jobs — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied render jobs records (render_seconds, sample_count). 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-02-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","render_seconds","sample_count"],"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},"sample_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"render_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","sample_count":1,"render_seconds":10},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","sample_count":2,"render_seconds":20},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","sample_count":3,"render_seconds":15},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","sample_count":4,"render_seconds":30}],"maxItems":200,"minItems":1},"field":{"enum":["render_seconds","sample_count"],"type":"string","default":"render_seconds"}}},"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_2h4QBkMAfLRZUkIqPoeuZ","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 render job records: string normalisation and a date-by-category pivot, for up to 200 rows.","exampleAgentPrompt":"Can you normalize the strings and create a date-by-category pivot for these render job records? I have rows with render_seconds and sample_count for asset groups like 'group-a' and 'group-b' across dates in October 2026 — run both reports on the render_seconds field.","exampleUseCases":[{"title":"Render pipeline performance breakdown","prompt":"I have 150 render job records with asset groups and dates from last quarter — can you normalize the group names and pivot the render_seconds by date and category so I can see which groups are slowest over time?"},{"title":"Sample count trend by asset group","prompt":"Here are my render job rows for November — please run string normalisation on them and do a date-by-category pivot on the sample_count field so I can track how sample volume changed per asset group each day."},{"title":"Studio render data cleanup and summary","prompt":"We've got raw render job data from multiple studios with inconsistent naming — normalize the strings and give me a date pivot grouped by asset_group for the render_seconds metric across these 80 rows."}],"resultDescription":"Two analytical reports delivered together: (1) a string-normalised version of the render job records, and (2) a date-by-category pivot table aggregating the chosen numeric field (render_seconds or sample_count) broken down by asset_group across dates. No external lookups or predictions are included.","failureModes":["Missing required fields (row_id, asset_group, date, render_seconds, sample_count) causes validation failure","Date strings not matching YYYY-MM-DD pattern are rejected","Exceeding 200 rows returns an error","Invalid field enum value (not render_seconds or sample_count) causes rejection","Empty rows array is rejected","Numeric values outside allowed range cause schema validation failure"],"whenToPreferThis":"Choose this endpoint when you have up to 200 render job records and need both string normalisation and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for creative/media pipeline analytics where you want to clean asset group names and summarise render performance or sample counts by date and category without any external API calls or live data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:57:35.708Z","isFirstParty":false,"canonicalSlug":"render-jobs-string-normalisation-date-by-category-pivot-08f56449"}