{"uid":"cap_OaE48TG88zhFz5DcMwxbz","slug":"render-jobs-daily-sum-series-iqr-outlier-flags-ba0f08f3","name":"Render Jobs Daily Sum Series + IQR Outlier Flags","description":"Render jobs — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags for supplied render jobs records (render_seconds, sample_count). 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-02-140?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_b2Inxe0DNX2TSFcEjdYEX","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":"Computes daily sum time series and IQR-based outlier flags for render job records (render_seconds or sample_count) across up to 200 rows, producing 2 independent analytical reports.","exampleAgentPrompt":"I have render job records with dates, render_seconds, and sample_count for groups A and B — can you compute the daily sum time series and flag any IQR outliers on render_seconds for up to these 200 rows?","exampleUseCases":[{"title":"Detect render time anomalies by day","prompt":"Here are our render job logs from last month with render_seconds per row — can you build a daily sum series and flag any days with IQR outliers on render_seconds so I can investigate unexpected spikes?"},{"title":"Monitor sample count trends over time","prompt":"I've got render job records across two asset groups with sample_count values by date — please compute the daily totals and use IQR outlier detection on sample_count to highlight any statistically unusual days."},{"title":"Audit render pipeline by asset group","prompt":"Can you take these 150 render job rows across groups alpha and beta, compute the daily sum series for render_seconds, and give me IQR outlier flags so I know which dates are statistically off?"}],"resultDescription":"Two independent analytical reports: (1) a daily sum time series aggregating the chosen field (render_seconds or sample_count) by date, and (2) IQR-based outlier flags identifying which dates or records fall outside the interquartile range. Both reports are derived entirely from the supplied input rows with no external data lookup.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows submitted — exceeds maximum row limit","Missing required fields (row_id, date, render_seconds, sample_count) — validation error","Invalid date format not matching YYYY-MM-DD pattern — rejected","Field selector not one of 'render_seconds' or 'sample_count' — enum validation failure","Payment of 0.01 USDC not provided or rejected — 402 response"],"whenToPreferThis":"Choose this endpoint when you have tabular render job records with dates and numeric metrics and need both a daily aggregate time series and IQR statistical outlier detection in a single low-cost call. Ideal for media/rendering pipelines where you want to spot anomalous days without building your own analytics stack. Best when your dataset is under 200 rows and you need two reports at once for $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:07.886Z","isFirstParty":false,"canonicalSlug":"render-jobs-daily-sum-series-iqr-outlier-flags-ba0f08f3"}