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Simulation Caches — Record Quality Audit + String Normalisation + Adjacent Row Changes
x402
Simulation Caches — Record Quality Audit + 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 2026-10-21).
Runs three local analytical reports on simulation cache records: record quality audit, string normalisation, and adjacent row change detection for cache_gb and simulation_seconds fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Simulation caches — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied simulation caches records (cache_gb, simulation_seconds). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports generated locally without external lookups: (1) a record quality audit flagging missing, out-of-range, or malformed values; (2) a string normalisation report highlighting inconsistent or non-standard string entries in fields like asset_group; and (3) an adjacent row changes report showing step-changes or anomalies between consecutive rows for the selected numeric field (cache_gb or simulation_seconds). Covers up to 200 rows per call.
Example use cases
Pre-pipeline cache data hygiene check
“Before I load these simulation cache records into the pipeline, can you audit their quality, normalise the string fields, and flag any sharp changes between adjacent rows in the cache_gb column? I have about 50 rows to check.”
Detecting anomalous simulation duration jumps
“I want to spot any sudden jumps or drops between consecutive rows in the simulation_seconds field — can you run the adjacent row changes report on my 120 simulation cache records, along with the full quality audit and string normalisation?”
Validating asset group naming consistency
“My simulation cache export might have inconsistent asset_group names — can you run the string normalisation report along with the record quality audit and adjacent row change detection on these 80 rows so I can clean them up?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-08-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have simulation cache tabular data (cache_gb, simulation_seconds per asset_group and date) and need all three of record quality auditing, string normalisation, and adjacent-row change detection in a single low-cost call. It is particularly suited for pre-pipeline data hygiene checks on up to 200 rows where no live lookups, predictions, or external actions are needed. Prefer it over general-purpose data quality tools when your data specifically follows this simulation cache schema.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, asset_group, date, cache_gb, simulation_seconds) cause row-level errors
- Invalid date format (must match YYYY-MM-DD pattern) causes validation failure
- Field selector not one of the accepted enum values (cache_gb, simulation_seconds) causes error
- Empty rows array causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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