{"uid":"cap_Ky3cqB5GaOvwBbdbu_8o9","slug":"render-jobs-record-quality-audit-string-normalisation-date-by-cfa63bd9","name":"Render Jobs — Record Quality Audit + String Normalisation + Date by Category Pivot","description":"Render jobs — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied render jobs records (render_seconds, sample_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-creative-02-206?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_l6Ge9qJkYIi0WWRqThL0Y","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":"Audits data quality, normalises strings, and produces a date-by-category pivot for up to 200 render job records (render_seconds, sample_count) in three bundled analytical reports.","exampleAgentPrompt":"Can you audit these render job records for data quality issues, normalise the string fields, and give me a date-by-category pivot on render_seconds? I have up to 50 rows with row_id, asset_group, date, render_seconds, and sample_count.","exampleUseCases":[{"title":"Render pipeline data quality check","prompt":"I have a batch of render job logs — about 80 rows with asset groups, dates, render_seconds, and sample_count — can you audit them for quality issues, normalise any messy string fields, and pivot render_seconds by date and asset group so I can see the breakdown?"},{"title":"Animation studio asset group pivot","prompt":"We've got 150 render job records from this week's animation run. Can you run a record quality audit on them, clean up the asset_group strings, and give me a pivot table of sample_count grouped by date and category?"},{"title":"QA report before render data ingestion","prompt":"Before I load these 200 render job rows into our data warehouse, can you check them for quality problems, normalise the string fields, and produce a date-by-category pivot on render_seconds so I can spot any outliers?"}],"resultDescription":"Three bundled analytical reports: (1) a record quality audit flagging missing values, out-of-range numbers, or malformed dates; (2) a string normalisation report showing cleaned asset_group and row_id values; and (3) a date-by-category pivot table summarising the chosen numeric field (render_seconds or sample_count) broken down by date and asset group — all derived from the supplied rows, with no external lookups.","failureModes":["Rows array empty or missing — returns validation error","More than 200 rows submitted — rejected with maxItems error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Numeric fields outside allowed range (±1,000,000,000,000) — rejected","Missing required fields (row_id, asset_group, date) — schema error","Invalid field enum value (not render_seconds or sample_count) — rejected"],"whenToPreferThis":"Choose this endpoint when you have render job records (with render_seconds and/or sample_count) and need three bundled analytical operations — quality audit, string normalisation, and date-category pivot — in a single low-cost call (0.01 USDC). It is ideal for offline/batch data preparation, pre-ingestion QA, or pipeline reporting when no live lookups or external enrichment are needed and the dataset is ≤200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:42.845Z","isFirstParty":false,"canonicalSlug":"render-jobs-record-quality-audit-string-normalisation-date-by-cfa63bd9"}