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Animation Budgets — Trailing Mean Series + IQR Outlier Flags
x402
Animation Budgets — Trailing Mean Series + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Computes trailing mean time series and IQR-based outlier flags for animation budget records (bone_count and frame_ms) across up to 200 rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Animation budgets — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied animation budgets records (bone_count, frame_ms). 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 independent analytical reports: (1) a trailing mean time series for the selected field (bone_count or frame_ms) computed over the specified rolling window, and (2) IQR-based outlier flags per row indicating which records fall outside the interquartile range. Both reports are computed locally against the supplied data with no external lookups.
Example use cases
Flagging anomalous frame render times
“I have 80 rows of animation budget data with frame_ms measurements across different game areas. Can you run a trailing mean series with a window of 4 and flag any IQR outliers in the frame_ms field so I can see which frames are unusually slow?”
Monitoring bone count spikes across game areas
“Run an IQR outlier analysis on my bone count data — I've got 120 animation budget records tagged by game area and date. Use a window of 6 and analyze the bone_count field so I can spot which areas are pushing too many bones.”
Reviewing animation budget trends before a release
“Before our build ships, I need to check our animation budget records for the last 3 months — 150 rows covering multiple game areas. Compute the trailing mean series with a window of 3 and flag bone_count outliers using IQR so we can catch regressions.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-07-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have animation budget records with bone_count and/or frame_ms measurements and need both a rolling mean trend series and IQR-based outlier detection in a single call for up to 200 rows. It is ideal for game developers auditing animation performance budgets, detecting frame time anomalies, or monitoring bone count health across game areas without requiring any external data sources or predictions.
Side effects
- Fewer than 1 row supplied — returns validation error
- More than 200 rows supplied — exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size larger than number of rows — trailing mean may be undefined for early rows
- Null values in bone_count or frame_ms — may affect IQR and mean calculations
- Invalid field enum value (not bone_count or frame_ms) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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