{"uid":"cap_zPu0-ajLLAduH1MFDggZI","slug":"render-farm-usage-adjacent-row-changes-explicit-missing-value-fill-660775d2","name":"Render Farm Usage – Adjacent Row Changes + Explicit Missing-Value Fill","description":"Render farm usage — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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-24-162?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},"field":{"enum":["node_hours","job_count"],"type":"string","default":"node_hours"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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__Tx5iR988TLRAyXil4t3p","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 adjacent row-over-row changes and fills missing values in render farm usage data (node_hours, job_count) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have render farm usage records for October 2026 across two asset groups, with some missing node_hours values — can you compute the adjacent row-over-row changes and fill those missing node_hours values using median imputation? I have about 4 rows with fields like date, asset_group, node_hours, and job_count.","exampleUseCases":[{"title":"Daily render farm usage delta tracking","prompt":"I have daily render farm logs for the past month across two asset groups — can you compute the adjacent row-by-row changes in node_hours and fill any missing values using median, so I can see how usage shifted from day to day?"},{"title":"Job count gap filling before reporting","prompt":"My render farm job_count data has some null entries mixed in — please fill those missing job_count values using zero-fill and also give me the adjacent row changes, I have about 50 rows with date and asset_group fields."},{"title":"Asset group usage comparison prep","prompt":"Before I run my quarterly render farm analysis, I need to clean up my 150-row usage dataset — fill missing node_hours with the mean and compute row-over-row differences in node_hours so I can compare usage across asset groups over time."}],"resultDescription":"Returns two analytical reports for the supplied render farm usage rows: (1) an adjacent row changes report showing differences between consecutive rows for the selected field (node_hours or job_count), and (2) an explicit missing-value fill report where null/missing values in the selected field are replaced using the chosen method (zero, mean, or median). Both reports are computed locally with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, asset_group, date, node_hours, job_count) on any row","Invalid date format not matching YYYY-MM-DD pattern","Invalid enum value for field or method parameters","Payment not included or insufficient — x402 payment required","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)"],"whenToPreferThis":"Choose this endpoint when you have render farm usage data (node_hours, job_count) with up to 200 rows and need both adjacent row-over-row change detection and explicit missing-value imputation in a single low-cost call. It is ideal for preprocessing render farm logs before reporting or visualization, especially when data has gaps. It does not perform live lookups, forecasting, or external enrichment — prefer it for purely local analytical transformation of supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:56:53.830Z","isFirstParty":false,"canonicalSlug":"render-farm-usage-adjacent-row-changes-explicit-missing-value-fill-660775d2"}