{"uid":"cap_bZg8Ki6EN7BfvZBae3ggX","slug":"lighting-setups-record-quality-audit-explicit-missing-value-fill-be427ed4","name":"Lighting Setups — Record Quality Audit + Explicit Missing-Value Fill","description":"Lighting setups — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied lighting setups records (light_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-11-019?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","light_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},"asset_group":{"type":"string","default":"","maxLength":80},"light_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"render_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","light_count":10,"render_seconds":1},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","light_count":20,"render_seconds":2},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","light_count":15,"render_seconds":3},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","light_count":30,"render_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["light_count","render_seconds"],"type":"string","default":"light_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_rJTVI-EAz3M3ORWG2CSa2","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":"Audits data quality and fills missing numeric values (light_count or render_seconds) in up to 200 lighting setup records using zero, mean, or median imputation.","exampleAgentPrompt":"Can you run a record quality audit and fill in any missing light_count values using the median for these lighting setup rows? I have about 50 rows with asset groups and dates — some light_count entries are null.","exampleUseCases":[{"title":"Clean lighting data before pipeline ingestion","prompt":"I have 120 lighting setup records with some null light_count values — can you audit the whole dataset for quality issues and fill the missing light_count fields using the median method so the data is ready to load into our pipeline?"},{"title":"Impute missing render times for render farm billing","prompt":"Some of my render_seconds values are missing across these 80 lighting setup rows. Can you do a quality audit and then fill those gaps using the mean, so I can use the cleaned data for billing calculations?"},{"title":"Validate completeness of daily lighting asset logs","prompt":"I've got 200 rows of daily lighting asset logs and I'm not sure how complete they are — can you run a quality audit and fill any missing light_count entries with zero so I can see exactly what's missing and have a fully populated dataset?"}],"resultDescription":"Returns two independent reports: (1) a record quality audit detailing completeness, null counts, and data issues across the submitted rows; and (2) an explicit missing-value fill report showing which cells were imputed for the specified field (light_count or render_seconds) using the chosen method (zero, mean, or median). No external data sources are used; all analysis is performed locally on the supplied rows.","failureModes":["Exceeding 200 rows returns a validation error","Rows missing required fields (row_id, date, light_count, render_seconds) are rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Invalid enum values for field or method parameters cause rejection","Empty rows array returns an error","Extremely large numeric values near boundary limits may produce unexpected statistics"],"whenToPreferThis":"Choose this endpoint when you need both a data quality audit AND missing-value imputation for lighting setup records (light_count, render_seconds) in a single low-cost call. It is ideal for 3D production pipelines where lighting datasets have null numeric fields and you want a structured quality report alongside cleaned data. Prefer this over generic data tools when your records are structured around asset_group, date, light_count, and render_seconds fields specifically.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:26.463Z","isFirstParty":false,"canonicalSlug":"lighting-setups-record-quality-audit-explicit-missing-value-fill-be427ed4"}