{"uid":"cap_Vl2p6yI3muEB6VZtaKajn","slug":"lighting-setups-data-quality-suite-audit-normalise-fill-467e2137","name":"Lighting Setups Data Quality Suite — Audit, Normalise & Fill","description":"Lighting setups — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied lighting setups records (light_count, render_seconds). 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-11-208?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_oRvNIfwsPPpJQPaE8hRXP","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 lighting-setup records: record quality audit, string normalisation, and explicit missing-value fill for light_count and render_seconds fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and missing-value fill (using median) on my lighting setup data — I have up to 50 rows with light_count and render_seconds fields — and give me all three reports?","exampleUseCases":[{"title":"Pre-pipeline data quality check","prompt":"Before I feed my lighting setup records into the rendering pipeline, can you audit them for quality issues, normalise any inconsistent strings, and fill in any missing light_count values using the median? I have about 80 rows with asset_group, date, light_count, and render_seconds."},{"title":"Missing render time imputation","prompt":"I've got a dataset of 120 lighting setup rows where some render_seconds values are null — can you fill those in using mean imputation and also run a quality audit and string normalisation on the whole dataset?"},{"title":"Studio data cleanup before reporting","prompt":"We're preparing a monthly lighting report and our 60-row dataset has messy strings and some missing light_count entries. Can you normalise the strings, fill the missing light_count values with zero, and give us a full record quality audit?"}],"resultDescription":"Three independent analytical reports delivered in a single response: (1) a record quality audit flagging issues per row, (2) a string normalisation report with cleaned field values, and (3) a missing-value fill report showing imputed values for the chosen field using the specified method (zero, mean, or median). All reports are computed locally with no external lookups.","failureModes":["Exceeding 200-row limit returns a validation error","Invalid date format (not YYYY-MM-DD) triggers schema rejection","Unknown fill method or field enum value causes request rejection","Empty rows array (minItems:1 not met) returns an error","Numeric values outside ±1,000,000,000,000 range fail schema validation"],"whenToPreferThis":"Choose this endpoint when you need a bundled three-in-one data quality pass (audit + normalise + impute) on lighting setup tabular data with light_count and render_seconds columns, for up to 200 rows at a fixed $0.01 cost. Ideal when you want offline, deterministic analysis with no external data calls and need all three reports in a single API call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:56:47.215Z","isFirstParty":false,"canonicalSlug":"lighting-setups-data-quality-suite-audit-normalise-fill-467e2137"}