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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-02-134?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},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_GW_x3YBXAxUpYdWfN2TRE","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":"Applies a numeric interval filter and performs a keyed dataset comparison across two supplied render job datasets (render_seconds, sample_count) for up to 200 rows each.","exampleAgentPrompt":"I have two batches of render job records — each up to 200 rows with render_seconds and sample_count — can you run a numeric interval filter on render_seconds and compare the two datasets row-by-row so I can see where the values diverge?","exampleUseCases":[{"title":"Render time outlier detection across sprints","prompt":"I've got two sets of render job logs from our last two production sprints, each with render_seconds and sample_count per asset group. Can you filter the render_seconds values by numeric interval and compare the two batches by row key so I can see which jobs ran significantly longer in the second sprint?"},{"title":"Asset group performance comparison by sample count","prompt":"Here are two render job datasets — baseline and updated pipeline — both tagged by asset_group and date. Please run the numeric interval filter on sample_count and do a keyed comparison so I can see which asset groups changed between the two runs."},{"title":"QA check on render job submissions","prompt":"I need to validate two submissions of render job records against each other — up to 150 rows each — using a numeric interval filter on render_seconds and a keyed dataset comparison. Can you produce both reports so I can flag any mismatches before we sign off?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric interval filter report segmenting records by the selected field (render_seconds or sample_count) into numeric ranges, and (2) a keyed dataset comparison report showing per-row differences between the two supplied datasets, both generated from locally supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, asset_group, date, render_seconds, sample_count) cause row-level errors","Invalid date format (not YYYY-MM-DD) triggers validation failure","Duplicate row_ids in keyed comparison may produce ambiguous match results","Null numeric values in render_seconds or sample_count may be excluded from interval filter","Mismatched row_ids between primary and comparison datasets reduce comparison coverage"],"whenToPreferThis":"Choose this endpoint when you have two discrete batches of render job records (up to 200 rows each) and need both a numeric range segmentation (interval filter) on render_seconds or sample_count AND a row-keyed comparison between the two datasets in a single call. It is purpose-built for render pipeline analytics — not for general-purpose tabular data, live render monitoring, or datasets exceeding 200 rows. Prefer it over generic analytics tools when your data is already structured with render job fields and you want two complementary reports for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:26.463Z","isFirstParty":false,"canonicalSlug":"render-jobs-numeric-interval-filter-keyed-dataset-comparison-7403a5a2"}