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Controller Sampling — String Normalisation + Secondary-Measure Weighted Mean
x402
Controller Sampling — String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Normalises game-area strings and computes a sample-count-weighted mean of input_latency_ms (or vice versa) across up to 200 controller sampling records, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Controller sampling — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied controller sampling records (input_latency_ms, sample_count). 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
Returns two analytical reports: (1) a string-normalised version of the game_area field across all submitted rows, and (2) a secondary-measure weighted mean of the selected field (input_latency_ms or sample_count) using the other measure as the weight, computed over up to 200 input rows.
Example use cases
Game latency audit by area
“I've got 50 controller sampling rows across group-a and group-b game areas with dates in October 2026, input latency values, and sample counts — can you normalise the game area names and compute the sample-count-weighted mean of input_latency_ms so I can compare latency performance across areas?”
Sample count distribution analysis
“Here are 120 controller sampling records with row IDs, dates, game areas, sample counts, and input latency values — please normalise the game_area strings and compute the weighted mean for the sample_count field, using input_latency_ms as the secondary weight measure.”
QA report for controller telemetry pipeline
“We're running QA on our controller telemetry pipeline and need two analytical reports: string normalisation on game_area labels and a secondary-measure weighted mean of input_latency_ms weighted by sample_count, for these 200 rows of controller sampling data.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-15-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have controller sampling records with game area labels that need string normalisation AND you need a weighted mean computation where one numeric measure (e.g. sample_count) weights the average of another (e.g. input_latency_ms). It is a fixed-price offline computation — no live lookup, prediction, or external action — making it ideal for batch analytical post-processing of gaming telemetry data up to 200 rows.
Side effects
- Exceeding 200 rows causes rejection — trim input to maxItems limit
- Missing required fields (row_id, date, game_area, input_latency_ms, sample_count) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error — at least 1 row required
- Invalid enum value for field parameter (must be input_latency_ms or sample_count) causes failure
- Payment not included or insufficient USDC causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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