{"uid":"cap_bukCpuRpNSJsfnQRMV-oM","slug":"render-jobs-numeric-distribution-and-missing-value-fill-0af3858e","name":"Render Jobs Numeric Distribution and Missing-Value Fill","description":"Render jobs — Numeric distribution + Explicit missing-value fill: Numeric distribution + 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-02-085?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_2ueAznUIQrXlqGe3P3FxV","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":"Analyzes up to 200 render job records to produce a numeric distribution report and fill missing values in render_seconds or sample_count using zero, mean, or median imputation.","exampleAgentPrompt":"I have a batch of render job records with dates, asset groups, render_seconds, and sample_counts — some values are missing. Can you give me a numeric distribution report for render_seconds and fill any missing render_seconds values using median imputation, across these 200 rows?","exampleUseCases":[{"title":"Audit render time distribution across pipeline","prompt":"Here are 150 render job records from our pipeline. Can you analyze the numeric distribution of render_seconds and also fill any missing render_seconds values using the mean so I can see the full picture?"},{"title":"Impute missing sample counts for reporting","prompt":"I've got a CSV of render job rows and some sample_count entries are null. Can you fill the missing sample_count values using zero and give me a distribution report on sample_count across all the rows?"},{"title":"Quality check render metrics by asset group","prompt":"Before I hand off these render job logs to the client, can you run a numeric distribution on render_seconds and fill any gaps with the median — I have about 80 rows covering two asset groups?"}],"resultDescription":"Returns two analytical reports for the supplied render job rows: (1) a numeric distribution summary (e.g. min, max, mean, median, percentiles, histogram buckets) for the specified field (render_seconds or sample_count), and (2) an explicit missing-value fill report showing which rows had nulls and what imputed values were assigned using the chosen method (zero, mean, or median). Covers up to 200 rows with no external data lookups.","failureModes":["Rows array is empty or missing — returns validation error","More than 200 rows supplied — request rejected or truncated","Invalid date format (not YYYY-MM-DD) — schema validation failure","Unrecognized fill method or field enum value — schema validation error","All values in the target field are null — distribution report may be empty or degenerate","row_id missing or exceeds 80 characters — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need both a numeric distribution summary and missing-value imputation for render job records (render_seconds or sample_count) in a single $0.01 call, without needing live data lookups, external integrations, or predictions. It is ideal for offline batch quality checks on up to 200 rows of rendering pipeline data where speed and low cost matter more than advanced ML-based imputation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:30.845Z","isFirstParty":false,"canonicalSlug":"render-jobs-numeric-distribution-and-missing-value-fill-0af3858e"}