{"uid":"cap_Hnj6JXHL39ZMaGc2kjHcD","slug":"render-jobs-string-normalisation-and-two-measure-correlation-f44e6acf","name":"Render Jobs String Normalisation and Two-Measure Correlation","description":"Render jobs — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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-02-033?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}}},"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_Qcj7o-NryVl2EAdm7qaGv","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 string normalisation and two-measure Pearson/rank correlation analysis on supplied render job records (render_seconds, sample_count) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you normalize the strings and run a two-measure correlation between render_seconds and sample_count on my render jobs data? I have rows for asset groups like 'group-a' and 'group-b' across dates in October 2026 — here are up to 200 rows with row_id, asset_group, date, render_seconds, and sample_count.","exampleUseCases":[{"title":"Render pipeline efficiency correlation","prompt":"I've got render job logs for our studio covering Q4 — each row has a job ID, asset group name, date, how many seconds the render took, and sample count. Can you normalize the asset group strings and run a two-measure correlation to see if sample count is actually driving render time?"},{"title":"Asset group render performance audit","prompt":"We have two asset groups, 'group-alpha' and 'group-beta', and I want to know if higher sample counts consistently lead to longer render times. Can you take my 80 render job records with render_seconds and sample_count and run the string normalisation plus two-measure correlation reports?"},{"title":"Statistical QA on rendering data batch","prompt":"Before I hand this render job dataset off to the team, I want to clean up the string fields and get a correlation report between render_seconds and sample_count. There are 150 rows, each tagged by row_id, asset group, and date — can you run both the normalisation and correlation analysis?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report cleaning and standardising the text fields (row_id, asset_group, date) across the supplied rows, and (2) a two-measure correlation report quantifying the statistical relationship between render_seconds and sample_count across all submitted render job records (up to 200 rows). No external lookups or predictions are made — analysis is performed entirely on the supplied data.","failureModes":["Fewer than 1 row submitted — rejected by minItems:1 constraint","More than 200 rows submitted — rejected by maxItems:200 constraint","Missing required fields (row_id, asset_group, date, render_seconds, sample_count) — validation error","Invalid date format (not YYYY-MM-DD) — schema pattern mismatch","Numeric values outside allowed range (±1,000,000,000,000) — schema bounds error","Payment not attached or insufficient — x402 payment required error","All render_seconds or sample_count values are null — correlation cannot be computed"],"whenToPreferThis":"Choose this endpoint when you have render job tabular data with render_seconds and sample_count fields and need both string normalisation of text fields and a statistical correlation between the two numeric measures in a single call. It is self-contained (no live lookups), cheap at $0.01 USDC for two reports, and handles up to 200 rows. Prefer this over general-purpose statistical tools when your data specifically matches the render jobs schema (row_id, asset_group, date, render_seconds, sample_count).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:08.950Z","isFirstParty":false,"canonicalSlug":"render-jobs-string-normalisation-and-two-measure-correlation-f44e6acf"}