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Simulation Caches — String Normalisation + Adjacent Row Changes
x402
Simulation Caches — String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Runs two local analytical reports on simulation cache records: string normalisation and adjacent row change detection, for up to 200 rows of cache_gb or simulation_seconds data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Simulation caches — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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 rows.
Output
Two analytical reports per call: (1) string normalisation results for the specified field across all supplied rows, and (2) adjacent row change detection showing deltas between consecutive rows — both computed locally without external lookups, covering up to 200 input rows.
Example use cases
Cache growth anomaly detection
“I have a week of simulation cache logs with daily cache_gb readings across two asset groups — can you check for any sudden jumps or drops between consecutive rows and also normalise the asset group labels?”
Simulation runtime drift analysis
“Here are 50 rows of simulation records with simulation_seconds values per date and asset group — run adjacent row change detection on simulation_seconds so I can see where runtimes shifted significantly between days.”
Data cleanup before pipeline ingestion
“Before I load these 120 simulation cache rows into our pipeline, can you normalise the string fields and flag any unusual adjacent changes in cache_gb so I can review them first?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-08-029?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, offline string normalisation and sequential change detection on simulation cache data (cache_gb or simulation_seconds) without any external API calls or live data lookups — ideal for pre-pipeline data hygiene checks on batches of up to 200 rows at low cost ($0.01 USDC for both reports).
Side effects
- More than 200 rows submitted — request rejected at maxItems validation
- Missing required fields (row_id, date, asset_group, cache_gb, simulation_seconds) cause schema validation failure
- Invalid date format (not YYYY-MM-DD) causes pattern validation failure
- field enum value outside [cache_gb, simulation_seconds] causes validation error
- Null numeric values may produce incomplete change detection output for affected rows
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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