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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-02-168?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}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"sample_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"render_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","sample_count":1,"render_seconds":10},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","sample_count":2,"render_seconds":20},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","sample_count":3,"render_seconds":15},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","sample_count":4,"render_seconds":30}],"maxItems":200,"minItems":1},"field":{"enum":["render_seconds","sample_count"],"type":"string","default":"render_seconds"},"other":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","render_seconds","sample_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","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":21},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","sample_count":3,"render_seconds":17},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","sample_count":4,"render_seconds":33}],"maxItems":200,"minItems":0},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_XEIWWbkFFdGvI2tsc83kT","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 a Base-100 indexed time series and a keyed dataset comparison for render job records (render_seconds, sample_count) across up to 200 rows.","exampleAgentPrompt":"Take my render job records for group-a and group-b from October 2026 and my baseline dataset, then run a base-100 series plus a keyed dataset comparison on render_seconds — up to 200 rows total — so I can see how current render times index against the baseline.","exampleUseCases":[{"title":"Benchmark current sprint render performance","prompt":"I have this week's render job rows with render_seconds and sample_count for our main asset groups, plus last sprint's data as a baseline. Run a base-100 series and keyed comparison on render_seconds so I can see which asset groups have regressed."},{"title":"Track render time trends across two studios","prompt":"Compare these two sets of render job records — one from Studio A and one from Studio B — using a base-100 index on render_seconds and a keyed dataset comparison, so I can see which studio's times are trending higher relative to the shared baseline."},{"title":"Audit sample count drift between pipeline versions","prompt":"I've got render job data from our old pipeline and our new pipeline, both with sample_count values. Run the base-100 series plus keyed dataset comparison on sample_count across both datasets so I can spot where the new pipeline deviates most."}],"resultDescription":"Returns two analytical reports: (1) a Base-100 indexed time series normalizing the selected field (render_seconds or sample_count) so the earliest or reference point equals 100, and (2) a keyed dataset comparison showing row-by-row or group-level differences between the primary and secondary datasets, including delta and relative change metrics. Up to 200 rows processed with no external lookups.","failureModes":["Missing required fields (row_id, date, render_seconds, sample_count) cause validation errors","More than 200 rows in either dataset triggers a payload rejection","Dates not matching YYYY-MM-DD pattern fail schema validation","Null values in the selected field may produce incomplete index calculations","Mismatched row_ids between primary and comparison dataset result in unmatched keyed comparisons","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you have structured render job records and need both a normalized time-series index (Base-100) and a side-by-side keyed comparison between two datasets in a single API call. It is ideal for creative pipeline analytics, render farm benchmarking, and sprint-over-sprint performance reviews where you supply all data locally and need no external data enrichment or predictions. Prefer it over single-report endpoints when you need both normalization and comparison simultaneously at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:41:21.214Z","isFirstParty":false,"canonicalSlug":"render-jobs-base-100-series-keyed-dataset-comparison-e92a70ad"}