{"uid":"cap_jL8qpIQK2m4rUpnzhL2Z8","slug":"render-jobs-string-normalisation-and-missing-value-fill-0561cecf","name":"Render Jobs String Normalisation and Missing-Value Fill","description":"Render jobs — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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-02-037?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_hy69ORs0idO3ucsHXoDh5","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":"Normalises string fields and fills missing values (via zero, mean, or median) in render job records containing render_seconds and sample_count, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 render job rows with asset_group, date, render_seconds, and sample_count fields — some render_seconds values are null. Can you normalise the strings and fill those missing render_seconds values using the median method, then give me the two analytical reports?","exampleUseCases":[{"title":"Impute missing render times in pipeline","prompt":"I've got a batch of render job records — some render_seconds entries are null. Can you fill those missing values using the median and also normalise the string fields across all rows? Here are up to 200 rows with row_id, asset_group, date, render_seconds, and sample_count."},{"title":"Zero-fill missing sample counts for audit","prompt":"My render job dataset has some null sample_count values I need to fix before an audit. Can you apply zero-fill to the missing sample_count field and normalise the strings, returning the two cleaned reports for my 150 rows?"},{"title":"Mean imputation for creative asset reporting","prompt":"Before I hand off this render job data to the reporting team, I need to impute the missing render_seconds using the mean and clean up any string inconsistencies. Can you process these render job records — about 80 rows with asset_group and date fields — and return the two analytical reports?"}],"resultDescription":"Two independent analytical reports covering string-normalised fields and explicitly imputed numeric values (render_seconds or sample_count) for up to 200 supplied render job rows, processed entirely from supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, asset_group) cause row-level errors","Invalid date format (not YYYY-MM-DD) may cause parsing failures","Numeric values outside allowed range (-1e12 to 1e12) rejected","Invalid method enum value (not zero/mean/median) causes validation error","Invalid field enum value (not render_seconds/sample_count) causes validation error"],"whenToPreferThis":"Choose this endpoint when you have render job records (with render_seconds and sample_count) that need both string normalisation and missing-value imputation in a single paid call, and you want two analytical reports delivered together at a fixed low cost. Prefer this over general-purpose data tools when your dataset is bounded at 200 rows and you specifically need the zero/mean/median fill options applied to render pipeline metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-31T00:44:22.343Z","isFirstParty":false,"canonicalSlug":"render-jobs-string-normalisation-and-missing-value-fill-0561cecf"}