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Animation Clips — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Animation Clips — Record Quality Audit + String Normalisation + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
Runs three local analytical reports on animation clip records (frame_count, duration_seconds): a data quality audit, string normalisation, and keyed comparison between two datasets — up to 200 rows each.
Price / call$0.01
Success—
StatusUnknown
How this service works
Animation clips — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied animation clips records (frame_count, duration_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
Returns three independent reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, and type errors in frame_count and duration_seconds; (2) a string normalisation report showing cleaned/standardised versions of string fields like asset_group; and (3) a keyed dataset comparison report showing row-level differences between the two supplied datasets, matched on row_id.
Example use cases
Pre-publish animation asset validation
“I've got a batch of animation clip records with frame counts and durations — can you run a quality audit on them to flag any nulls, out-of-range values, or formatting problems before I publish them to the pipeline? Here's my dataset with fields row_id, asset_group, date, frame_count, and duration_seconds.”
Reconciling two animation export batches
“I exported our animation clips twice from different systems and I need to compare the two datasets row by row — can you do a keyed comparison to tell me which rows differ in frame_count or duration_seconds between the two batches?”
Normalising asset group names before ingestion
“Some of our animation clip records have messy asset_group strings with inconsistent casing and whitespace. Can you normalise those string fields across my 150-row dataset so they're clean and consistent before I ingest them?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-07-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need offline, local analytical processing of animation clip tabular data — specifically when you want all three of data quality auditing, string normalisation, and keyed two-dataset comparison bundled in a single low-cost call. Ideal for media asset pipeline validation, pre-ingestion cleaning, or reconciliation tasks where no external lookup or live data is needed and batch size is under 200 rows.
Side effects
- Exceeding 200 rows in either dataset returns a validation error
- Missing required fields (row_id, asset_group, date, frame_count, duration_seconds) causes schema rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Submitting fewer than 1 row in the rows array returns a minItems error
- Numeric values outside ±1,000,000,000,000 are rejected by schema bounds
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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