{"uid":"cap_OyCp2Z7CTOxE44uvDtVtR","slug":"texture-baking-analytics-iqr-outlier-flags-secondary-measure-weighted-80e7d922","name":"Texture Baking Analytics — IQR Outlier Flags + Secondary-Measure Weighted Mean","description":"Texture baking — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean for supplied texture baking records (bake_seconds, texture_pixels). 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-10-177?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","bake_seconds","texture_pixels"],"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},"bake_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"texture_pixels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","bake_seconds":10,"texture_pixels":1},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","bake_seconds":20,"texture_pixels":2},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","bake_seconds":15,"texture_pixels":3},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","bake_seconds":30,"texture_pixels":4}],"maxItems":200,"minItems":1},"field":{"enum":["bake_seconds","texture_pixels"],"type":"string","default":"bake_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_1cWtEaoTG0iGF8KX6Rly0","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 texture baking records to flag IQR-based outliers and compute a secondary-measure weighted mean, delivering two statistical reports for up to 200 rows.","exampleAgentPrompt":"Can you analyze these texture baking records for our asset group and flag any IQR outliers in bake_seconds, then give me the weighted mean using texture_pixels as the secondary measure? I have about 50 rows with row_id, asset_group, date, bake_seconds, and texture_pixels.","exampleUseCases":[{"title":"QA outlier detection for baking pipeline","prompt":"I have 80 texture baking records from our latest game asset batch — can you flag any bake_seconds values that are IQR outliers and also compute the texture_pixels-weighted mean for bake_seconds? The rows have row_id, asset_group, date, bake_seconds, and texture_pixels fields."},{"title":"Weighted summary for art team reporting","prompt":"Our art team needs a report on this week's texture baking run — please compute the weighted mean of bake_seconds weighted by texture_pixels for about 120 rows, and flag any outliers using the IQR method so we know which assets to investigate."},{"title":"Anomaly triage across multiple asset groups","prompt":"I've got texture baking data for three different asset groups across 60 rows — can you run IQR outlier flagging on texture_pixels and give me the secondary-measure weighted mean so I can see which groups are behaving abnormally?"}],"resultDescription":"Two statistical reports: (1) IQR outlier flags indicating which rows are statistical outliers for the selected primary field, and (2) a secondary-measure weighted mean across the dataset, both computed locally from the supplied texture baking rows without any external data lookups.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Missing required fields (row_id, bake_seconds, texture_pixels, date, asset_group) cause validation errors","Invalid date format (non-YYYY-MM-DD) causes schema rejection","Null values in the primary field may affect IQR computation or be excluded","Invalid field enum value (not 'bake_seconds' or 'texture_pixels') causes error","Payment of 0.01 USDC not fulfilled via x402 protocol — request blocked"],"whenToPreferThis":"Choose this endpoint when you need simultaneous IQR-based outlier detection and a secondary-measure weighted mean on texture baking data (bake_seconds, texture_pixels) with up to 200 rows, all in a single low-cost call. It is purpose-built for 3D/game asset pipeline analytics where both anomaly flagging and weighted aggregation are needed together without external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-27T00:44:50.304Z","isFirstParty":false,"canonicalSlug":"texture-baking-analytics-iqr-outlier-flags-secondary-measure-weighted-80e7d922"}