{"uid":"cap_cKehEhtoWUek5cOL9nHnj","slug":"render-jobs-base-100-series-explicit-missing-value-fill-6898c81e","name":"Render Jobs Base-100 Series + Explicit Missing-Value Fill","description":"Render jobs — Base-100 series + Explicit missing-value fill: Base-100 series + 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-02-169?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_rjr2sYhF8thwgdsFQOTTc","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":"Computes a base-100 indexed time series and fills missing values for render job data (render_seconds, sample_count) across up to 200 rows.","exampleAgentPrompt":"I have 50 render job records with render_seconds and some nulls — can you index them to a base-100 series starting from row 0 and fill the missing render_seconds values using median imputation?","exampleUseCases":[{"title":"Baseline render time comparison","prompt":"I've got 120 rows of daily render job data across two asset groups and I want to see render_seconds indexed to 100 at row 0 so I can compare performance trends — please also fill any missing render_seconds values using the median."},{"title":"Missing sample count imputation","prompt":"My render job dataset has nulls scattered in the sample_count column — can you fill those gaps with zero and also produce the base-100 indexed series for sample_count starting at row index 5?"},{"title":"Asset group render performance indexing","prompt":"Here are 80 render job records for our animation pipeline — index the render_seconds to base 100 from the first row and use mean fill for any missing values so I can plot relative performance over time."}],"resultDescription":"Returns two analytical reports for the submitted render job rows: (1) a base-100 indexed series where the specified base_index row equals 100 and all other rows are expressed relative to it, and (2) the same dataset with missing values in the chosen field explicitly filled using the selected method (zero, mean, or median). Both reports cover up to 200 rows and are computed locally without external lookups.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Invalid date format not matching YYYY-MM-DD pattern","base_index out of range (0–199) or exceeding row count","Numeric values outside allowed range (-1e12 to 1e12)","All values in selected field are null — mean/median fill undefined","Missing required fields (row_id, asset_group, date, render_seconds, sample_count) per row"],"whenToPreferThis":"Choose this endpoint when you have render job tabular data with a time dimension and need both a relative-performance index (base-100 normalization) and missing-value imputation in a single call. It is ideal for creative/media pipeline analytics where render_seconds or sample_count metrics need to be compared across dates or asset groups and gaps must be handled before visualization. Prefer it over general analytics tools when you need both operations together cheaply ($0.01 USDC for up to 200 rows) and without external data dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:56:41.541Z","isFirstParty":false,"canonicalSlug":"render-jobs-base-100-series-explicit-missing-value-fill-6898c81e"}