{"uid":"cap_7is96DEy64lziclXViLOh","slug":"texture-baking-analytics-category-frequencies-iqr-outlier-flags-452b847b","name":"Texture Baking Analytics — Category Frequencies + IQR Outlier Flags","description":"Texture baking — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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-10-107?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_5plULouGtaMw8kqod8nGO","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 category frequency distributions and IQR-based outlier flags for texture baking records (bake_seconds or texture_pixels) across up to 200 rows.","exampleAgentPrompt":"I have texture baking records with bake_seconds and asset_group columns — can you run category frequency counts and IQR outlier detection on the bake_seconds field for my 150 rows of data?","exampleUseCases":[{"title":"Detect slow outlier bake jobs","prompt":"I've got a batch of 120 texture baking records with bake times — can you flag any bake_seconds values that are IQR outliers and also show me how many records fall into each asset group?"},{"title":"Audit pixel count anomalies by group","prompt":"Run frequency and IQR outlier analysis on my texture baking dataset using the texture_pixels field — I want to see which asset groups dominate and which pixel values look statistically extreme."},{"title":"QA render pipeline by asset group","prompt":"I have 80 rows of texture bake job data across groups like 'characters', 'environments', and 'props' — give me the category frequency breakdown and flag any IQR outliers in bake_seconds so I can spot problem assets."}],"resultDescription":"Returns two analytical reports: (1) category frequency counts broken down by asset_group, showing how many records fall in each category; and (2) IQR-based outlier flags identifying which rows have extreme values for the selected numeric field (bake_seconds or texture_pixels). Both reports are computed locally without external lookups.","failureModes":["Exceeding 200 rows returns an error or truncation","Missing required fields (row_id, bake_seconds, texture_pixels) cause validation failure","Invalid date format (non-YYYY-MM-DD) triggers schema rejection","Null values in the selected numeric field may affect IQR calculation","Invalid enum value for 'field' parameter causes rejection"],"whenToPreferThis":"Choose this endpoint when you have texture baking production data and need both categorical distribution analysis (how many records per asset group) and statistical outlier detection (IQR method) in a single low-cost call. Ideal for 3D pipeline QA, render farm audits, or asset management workflows where you want to identify anomalous bake times or texture sizes without setting up a full analytics stack.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-27T00:44:49.015Z","isFirstParty":false,"canonicalSlug":"texture-baking-analytics-category-frequencies-iqr-outlier-flags-452b847b"}