{"uid":"cap_kLcUnXPYUdVQ-QtnFoE12","slug":"simulation-caches-numeric-interval-filter-keyed-dataset-comparison-dfe080be","name":"Simulation Caches — Numeric Interval Filter + Keyed Dataset Comparison","description":"Simulation caches — Numeric interval filter + Keyed dataset comparison: Numeric interval filter + Keyed dataset comparison for supplied simulation caches records (cache_gb, simulation_seconds). 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 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a numeric interval filter and keyed dataset comparison to simulation cache records (cache_gb, simulation_seconds) for up to 200 rows across two independent analytical reports.","exampleAgentPrompt":"I have two sets of simulation cache records (up to 200 rows each) with fields like cache_gb and simulation_seconds — can you run a numeric interval filter and a keyed comparison between the two datasets using cache_gb as the target field?","exampleUseCases":[{"title":"Comparing cache growth between asset groups","prompt":"I have two snapshots of our simulation cache data across group-a and group-b, each with up to 150 rows including cache_gb and simulation_seconds — run a numeric interval filter on cache_gb and compare the two datasets row-by-row using the row IDs as keys."},{"title":"Detecting simulation time regressions","prompt":"Can you take my before-and-after simulation run records (up to 200 rows each) and run a keyed comparison on simulation_seconds along with an interval filter so I can see which rows changed significantly between the two runs?"},{"title":"Auditing cache size distributions by date","prompt":"I want to filter our daily simulation cache records from October 2026 by numeric intervals on cache_gb and compare them against last month's baseline dataset — both sets have the same row structure with asset_group and date columns."}],"resultDescription":"Two independent analytical reports: (1) a numeric interval filter report categorizing rows by which interval their selected field (cache_gb or simulation_seconds) falls into, and (2) a keyed dataset comparison report showing row-level differences between the primary and secondary datasets matched by row_id. Results cover up to 200 rows and are computed locally without external lookups or predictions.","failureModes":["Exceeding 200 rows in either dataset returns a validation error","Missing required fields (row_id, asset_group, date, cache_gb, simulation_seconds) causes row-level rejection","Invalid date format (non-YYYY-MM-DD) triggers a pattern validation failure","Numeric values outside the allowed range (-1000000000000 to 1000000000000) are rejected","Mismatched row_ids between primary and secondary datasets may result in unmatched comparison entries","Empty rows array (minItems: 1 not met) returns an input validation error"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously apply numeric interval segmentation and perform a keyed row-level comparison on simulation cache data (cache_gb or simulation_seconds) within a single low-cost call. It is best suited for offline batch analysis of up to 200 simulation records where no live data lookup, prediction, or external API call is needed. Prefer it over general-purpose analytics tools when working specifically with simulation cache datasets structured around asset_group, date, and numeric cache/time metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:57:30.511Z","isFirstParty":false,"canonicalSlug":"simulation-caches-numeric-interval-filter-keyed-dataset-comparison-dfe080be"}