{"uid":"cap_prmg6DZr2a05d6mGAhFJc","slug":"render-jobs-record-quality-audit-string-normalisation-iqr-outlier-flags-f2a745c8","name":"Render Jobs — Record Quality Audit + String Normalisation + IQR Outlier Flags","description":"Render jobs — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied render jobs records (render_seconds, sample_count). 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-02-203?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","render_seconds","sample_count"],"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},"sample_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","sample_count":1,"render_seconds":10},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","sample_count":2,"render_seconds":20},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","sample_count":3,"render_seconds":15},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","sample_count":4,"render_seconds":30}],"maxItems":200,"minItems":1},"field":{"enum":["render_seconds","sample_count"],"type":"string","default":"render_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_XLwpmHV6EuQqXRKu6YO2i","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":"Performs three bundled analytical reports on render job records: record quality audit, string normalisation, and IQR-based outlier flagging for render_seconds and sample_count fields.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and IQR outlier check on these render job records — I have up to 50 rows with fields like row_id, asset_group, date, render_seconds, and sample_count — and flag anything suspicious in the render_seconds column?","exampleUseCases":[{"title":"Outlier detection in render pipeline","prompt":"I have a batch of 120 render job records with render_seconds and sample_count values — can you run IQR outlier flagging on the render_seconds field and tell me which rows are statistical anomalies?"},{"title":"Data quality audit before reporting","prompt":"Before I send this render job dataset to the client, can you do a record quality audit on these 80 rows to check for missing values, malformed dates, and bad strings in the asset_group field?"},{"title":"Normalising asset group labels","prompt":"My render job export has inconsistent capitalisation and spacing in the asset_group column across 150 rows — can you run string normalisation on it so the labels are clean and consistent?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit identifying missing, null, or malformed values per row; (2) string normalisation results for text fields like asset_group; and (3) IQR-based outlier flags indicating which rows fall outside the interquartile range for the selected numeric field (render_seconds or sample_count). All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, date, render_seconds, sample_count) cause row-level errors","Invalid date format (not YYYY-MM-DD) fails pattern validation","Numeric fields outside allowed range trigger schema validation errors","Empty rows array returns an error response","field selector not one of 'render_seconds' or 'sample_count' causes enum validation failure"],"whenToPreferThis":"Choose this endpoint when you need three bundled data quality operations — audit, normalisation, and outlier detection — on render job records in a single low-cost call. It is ideal for pre-analysis data cleaning of render pipeline exports with up to 200 rows. Prefer it over single-purpose quality tools when you need all three report types simultaneously and the data is already available locally without needing live lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:13.533Z","isFirstParty":false,"canonicalSlug":"render-jobs-record-quality-audit-string-normalisation-iqr-outlier-flags-f2a745c8"}