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Physics Scenes — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Physics Scenes — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Runs three local analytical reports on physics scene records (body_count, step_ms): a data quality audit, string normalisation, and a trailing mean time series — all offline, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Physics scenes — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied physics scenes records (body_count, step_ms). 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: (1) a record quality audit flagging missing values, out-of-range entries, and structural issues in the supplied rows; (2) a string normalisation report for categorical string fields like game_area; (3) a trailing mean series for the selected numeric field (body_count or step_ms) over the specified rolling window. All computed locally from submitted data — no external data sources involved. Up to 200 rows per call.
Example use cases
Physics engine data quality check
“Run a full record quality audit, string normalisation, and trailing mean series with window 2 for body_count on these physics scene rows: row-1 (2026-10-01, group-a, body_count 10, step_ms 1.5), row-2 (2026-10-02, group-b, body_count null, step_ms 2.0), row-3 (2026-10-03, group-a, body_count 15, step_ms 3.1). Flag any missing or malformed values.”
Rolling average for simulation step time
“I need the trailing mean series for step_ms across my simulation log using a window of 5. Here are my rows: row-a through row-e covering 2026-09-01 to 2026-09-05 in areas zone-1 and zone-2 with step_ms values 4.2, 3.8, 5.1, 4.9, 6.0 and body_count values 12, 14, 11, 15, 13. Also run the quality audit and string normalisation.”
Game area string normalisation for analytics pipeline
“Before I feed these physics scene records into my analytics pipeline, can you normalise the game_area strings and audit data quality? I have 6 rows from 2026-08-01 to 2026-08-06 with areas like 'Group A', 'group-a', 'GROUP_A', body_count ranging 5–50, and step_ms 1–10. Use body_count as the field and window size 2 for the trailing mean.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-08-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytics reports — quality audit, string normalisation, and trailing mean series — specifically for physics scene datasets containing body_count and step_ms fields, at low cost ($0.01 for all three). Ideal when you want offline computation with no external lookups, predictions, or API calls, and your dataset fits within 200 rows. Prefer alternatives if you need live data enrichment, prediction, or reports on different domain schemas (e.g. financial, municipal, or project data).
Side effects
- Fewer than 1 row submitted returns a validation error
- More than 200 rows submitted exceeds the row limit and is rejected
- Invalid date format (not YYYY-MM-DD) causes a schema validation error
- Window size larger than the number of submitted rows may produce empty or truncated trailing mean results
- Numeric fields outside the allowed range (-1e12 to 1e12) are rejected
- Missing required fields (row_id, game_area, date, body_count, step_ms) cause schema validation failure
- Non-integer window value causes a type validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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