{"uid":"cap_hTZgsVGWS_1-rhQMxMLJ-","slug":"render-farm-usage-string-normalisation-two-measure-correlation-99d350b8","name":"Render Farm Usage — String Normalisation + Two-Measure Correlation","description":"Render farm usage — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied render farm usage records (node_hours, job_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-24-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","node_hours","job_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"job_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"node_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","job_count":1,"node_hours":10,"asset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","job_count":2,"node_hours":20,"asset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","job_count":3,"node_hours":15,"asset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","job_count":4,"node_hours":30,"asset_group":"group-b"}],"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_ZWno025RaxJvWFx1heOTl","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":"Normalises strings and computes two-measure correlation analysis for render farm usage records (node_hours, job_count) across up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you normalize the string labels and compute the correlation between node_hours and job_count for my render farm usage records? I have 4 rows: row-1 on 2026-10-01 with group-a, 10 node hours, 1 job; row-2 on 2026-10-02 with group-b, 20 node hours, 2 jobs; row-3 on 2026-10-03 with group-a, 15 node hours, 3 jobs; row-4 on 2026-10-04 with group-b, 30 node hours, 4 jobs.","exampleUseCases":[{"title":"Render farm efficiency correlation","prompt":"I have a month of render farm logs — can you normalize the asset group names and tell me how strongly correlated node_hours and job_count are across those rows? I'll paste the data: row-1, group-a, 2026-10-01, 10 node hours, 1 job; row-2, group-b, 2026-10-02, 40 node hours, 5 jobs; row-3, group-c, 2026-10-03, 25 node hours, 3 jobs."},{"title":"VFX studio workload analysis","prompt":"Our VFX pipeline team needs to understand if job count and node hours move together across different asset groups. Can you run string normalization and a two-measure correlation on this render farm data? Here are 5 rows with row_id, asset_group, date, node_hours, and job_count."},{"title":"Render billing data cleanup and stats","prompt":"We track render jobs by group and date but the asset group names are inconsistently formatted. Can you normalize those strings and also compute the correlation between node_hours and job_count so we understand the relationship? I have about 50 rows of data."}],"resultDescription":"Two analytical reports: (1) a string normalisation report for asset_group and other string fields, and (2) a two-measure correlation analysis showing the statistical relationship between node_hours and job_count across the supplied rows. No external lookups or predictions — results are computed entirely from the submitted data.","failureModes":["Rows array empty or missing: validation error","More than 200 rows submitted: request rejected","Invalid date format (not YYYY-MM-DD): validation error","node_hours or job_count outside allowed numeric range: validation error","row_id missing or empty: validation error","All node_hours or job_count values null: correlation report may be undefined or return NaN"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and a two-measure correlation report on render farm usage data (node_hours vs job_count) in a single call for up to 200 rows, with no need for live lookups, predictions, or external integrations. It is cost-effective at $0.01 USDC for two bundled reports and is ideal for offline/batch analytical workflows on already-collected render job logs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:50:53.041Z","isFirstParty":false,"canonicalSlug":"render-farm-usage-string-normalisation-two-measure-correlation-99d350b8"}