{"uid":"cap_AUnvOA7lnBFw2XXj6UDb7","slug":"lighting-setups-data-quality-analytics-audit-normalisation-grouped-111b5f7b","name":"Lighting Setups Data Quality & Analytics — Audit, Normalisation & Grouped Summary","description":"Lighting setups — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary 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-11-194?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"}}},"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_EQ7NOZP4-5MwPR1QdYJnJ","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 grouped numeric summary for light_count and render_seconds fields.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and grouped numeric summary on my lighting setup records? I have rows with asset_group, date, light_count, and render_seconds — here are up to 50 rows, and I want the summary grouped by light_count.","exampleUseCases":[{"title":"Pre-render pipeline data validation","prompt":"I've got a batch of lighting setup records from our render farm — each row has a row_id, asset_group, date, light_count, and render_seconds. Can you audit their quality, normalise the asset group strings, and give me a grouped numeric summary on render_seconds so I can spot any bad data before we kick off the next render job?"},{"title":"Asset group render time analysis","prompt":"Here are 80 rows of lighting config data with light_count and render_seconds per asset group. Can you group and summarise the render_seconds field for me, clean up any inconsistent asset group names, and flag any quality issues in the records?"},{"title":"Lighting data QA before handoff","prompt":"Before I hand off this lighting dataset to the VFX team, I want to make sure it's clean. Can you run a record quality audit, normalise the string fields, and produce a grouped summary for light_count across all the asset groups? I've got about 120 rows to check."}],"resultDescription":"Returns three reports: (1) a record quality audit flagging missing, malformed, or out-of-range values; (2) normalised/standardised string representations of string fields like asset_group; and (3) a grouped numeric summary (e.g. mean, min, max, count) for the selected numeric field (light_count or render_seconds) broken out by asset_group. All computed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, asset_group, date, light_count, render_seconds) causes row-level errors","Invalid date format (not YYYY-MM-DD) fails schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array is rejected","Invalid field enum value causes request failure"],"whenToPreferThis":"Choose this endpoint when you need a combined data quality audit, string normalisation, and grouped statistical summary for lighting setup or 3D render records in a single low-cost call. It is ideal for creative/VFX pipeline data hygiene checks on batches up to 200 rows, where no external lookup or prediction is needed and all computation is local. It is not suitable for real-time monitoring, ML prediction, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:57:05.727Z","isFirstParty":false,"canonicalSlug":"lighting-setups-data-quality-analytics-audit-normalisation-grouped-111b5f7b"}