{"uid":"cap_paw8d9C3TkYxKDmjyrO1p","slug":"texture-baking-data-quality-distribution-analyzer-52f68a91","name":"Texture Baking Data Quality & Distribution Analyzer","description":"Texture baking — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution for supplied texture baking records (bake_seconds, texture_pixels). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-creative-10-193?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_oJtXWdtJKeG7vI2h4MEKY","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":"Runs three local analytical reports on texture baking records: record quality audit, string normalisation, and numeric distribution for bake_seconds and texture_pixels fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and numeric distribution analysis on my texture baking data? I have 50 rows with fields row_id, asset_group, date, bake_seconds, and texture_pixels — use bake_seconds as the numeric field.","exampleUseCases":[{"title":"Texture bake log quality check","prompt":"I've got 120 rows of texture baking records with asset groups and bake times — can you audit them for quality issues, normalize the string fields, and show me the numeric distribution of bake_seconds?"},{"title":"Pixel count distribution for asset review","prompt":"Run a quality audit, string normalisation, and numeric distribution on my texture baking dataset using texture_pixels as the main numeric field. I have about 80 rows across multiple asset groups."},{"title":"Pre-delivery data validation for 3D pipeline","prompt":"Before I hand off this texture baking log to the render farm team, can you check it for record quality problems, normalize any inconsistent strings, and give me a distribution breakdown of the bake_seconds values? There are 40 rows total."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing values, invalid formats, or anomalies; (2) a string normalisation report cleaning and standardising text fields like asset_group; (3) a numeric distribution summary (e.g. min, max, mean, histogram buckets) for the selected numeric field (bake_seconds or texture_pixels). All computed locally with no external calls.","failureModes":["More than 200 rows submitted — request rejected","Missing required fields (row_id, date, bake_seconds, or texture_pixels) per row — validation error","Invalid date format (not YYYY-MM-DD) — schema rejection","Numeric values outside allowed range (±1,000,000,000,000) — validation error","Empty rows array — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need a bundled three-in-one local analytical pass (quality audit + string normalisation + numeric distribution) on texture baking records without requiring live lookups, predictions, or external data enrichment. It is cost-effective at $0.01 for all three reports on up to 200 rows and is ideal for 3D pipeline data hygiene checks before downstream processing.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:19.317Z","isFirstParty":false,"canonicalSlug":"texture-baking-data-quality-distribution-analyzer-52f68a91"}