{"uid":"cap_l8-RkTGCPvgzNwg8dswAb","slug":"compositing-graphs-secondary-measure-weighted-mean-explicit-missing-caf0b614","name":"Compositing Graphs — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill","description":"Compositing graphs — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill for supplied compositing graphs records (node_count, render_seconds). 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-09-187?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","node_count","render_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"node_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"render_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","node_count":10,"asset_group":"group-a","render_seconds":1},{"date":"2026-10-02","row_id":"row-2","node_count":20,"asset_group":"group-b","render_seconds":2},{"date":"2026-10-03","row_id":"row-3","node_count":15,"asset_group":"group-a","render_seconds":3},{"date":"2026-10-04","row_id":"row-4","node_count":30,"asset_group":"group-b","render_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["node_count","render_seconds"],"type":"string","default":"node_count"},"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_ZWpoXEMx5109BfaKdyiGL","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 secondary-measure weighted mean and fills missing values in compositing graph records (node_count, render_seconds), processing up to 200 rows across 2 independent analytical reports.","exampleAgentPrompt":"I have compositing graph records with node_count and render_seconds for asset groups — can you compute a secondary-measure weighted mean of render_seconds (weighted by node_count) and fill any missing values using median imputation? Here are up to 200 rows across my groups.","exampleUseCases":[{"title":"Render pipeline performance aggregation","prompt":"I've got 150 rows of compositing graph data with node_count and render_seconds per asset group and some nulls in the render_seconds column — can you fill those missing values using the mean method and then compute a node_count-weighted mean of render_seconds for each group?"},{"title":"Missing data cleanup for compositing audit","prompt":"My compositing pipeline export has gaps in the node_count field across several dates — can you fill those nulls with zeros and give me a weighted mean report using render_seconds as the secondary measure?"},{"title":"Asset group rendering benchmark comparison","prompt":"I want to compare rendering performance across asset groups A and B using my compositing graph records — can you run a secondary-measure weighted mean on render_seconds weighted by node_count, using median fill for any missing values, across these 80 rows?"}],"resultDescription":"Returns up to 2 analytical reports per call: a secondary-measure weighted mean result (where one numeric field weights the other) and an explicit missing-value fill result for the supplied compositing graph records. Reports include per-group aggregated statistics covering node_count and render_seconds fields across up to 200 input rows.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, asset_group) cause row rejection","Invalid date format (non-YYYY-MM-DD) triggers schema validation failure","Numeric values outside ±1,000,000,000,000 are rejected","All-null field values may produce undefined weighted mean output","Malformed JSON input returns a 400-level error"],"whenToPreferThis":"Choose this endpoint when you have compositing graph tabular data (node_count and render_seconds) with potential missing values and need both weighted mean aggregation and null imputation in a single call at low cost ($0.01 USDC for 2 reports, up to 200 rows). Prefer this over generic data tools when working specifically with rendering pipeline metrics organized by asset group and date, and when you need local analytical computation without any external lookups or live data dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-31T06:50:09.538Z","isFirstParty":false,"canonicalSlug":"compositing-graphs-secondary-measure-weighted-mean-explicit-missing-caf0b614"}