{"uid":"cap_-pJxbGuWCNdZ3SKvm-yK5","slug":"texture-baking-iqr-outlier-flags-date-by-category-pivot-43901ea6","name":"Texture Baking IQR Outlier Flags + Date by Category Pivot","description":"Texture baking — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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-10-178?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_J7i2Yt6Ampvg0yDOebMAe","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 supplied texture baking records to produce IQR-based outlier flags and a date-by-category pivot table, delivered as two independent analytical reports.","exampleAgentPrompt":"I have texture baking records for my asset pipeline — can you run IQR outlier detection on bake_seconds and also pivot the results by date and asset group? Here are up to 200 rows with row_id, asset_group, date, bake_seconds, and texture_pixels.","exampleUseCases":[{"title":"Outlier detection in bake time logs","prompt":"I have 150 rows of texture baking logs from our rendering farm — each has a row_id, asset_group, date, bake_seconds, and texture_pixels. Can you flag any statistical outliers in bake_seconds using IQR analysis and also give me a pivot of bake_seconds by date and asset_group?"},{"title":"Quality audit on texture pixel counts","prompt":"Here are 80 texture baking records from last month's 3D production run. Run IQR outlier flagging on texture_pixels so I can spot the bad renders, and pivot the texture_pixels values by date and category for a summary report."},{"title":"Pipeline anomaly report for art team","prompt":"Our art pipeline generated 200 texture bake records across group-a and group-b this week. Flag any IQR outliers in bake_seconds and create a date-by-category pivot table so I can share a clean anomaly report with the team."}],"resultDescription":"Returns two independent analytical reports: (1) IQR outlier flags identifying which rows have values statistically outside the interquartile range for the selected field, and (2) a date-by-category pivot table summarizing the chosen metric (bake_seconds or texture_pixels) across asset groups over time. Results are computed locally from supplied data with no external lookups.","failureModes":["Fewer than 1 row submitted — minimum of 1 row required","More than 200 rows submitted — maximum row limit exceeded","Missing required fields (row_id, asset_group, date, bake_seconds, texture_pixels) on any row","Invalid date format not matching YYYY-MM-DD pattern","Invalid field enum value — must be 'bake_seconds' or 'texture_pixels'","Null values for numeric fields may reduce IQR accuracy if too prevalent"],"whenToPreferThis":"Choose this endpoint when you have up to 200 texture baking records and need both IQR-based outlier detection and a date-by-category pivot in a single low-cost call. It is ideal for 3D production pipelines monitoring render farm performance, where you want statistical anomaly flags alongside a structured time-series breakdown by asset group — all computed from your own supplied data without any external API dependency.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-27T00:44:12.449Z","isFirstParty":false,"canonicalSlug":"texture-baking-iqr-outlier-flags-date-by-category-pivot-43901ea6"}