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Label Review Analytics — String Normalisation + Keyed Dataset Comparison
x402
Label Review Analytics — 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 2026-10-15).
Runs string normalisation and keyed dataset comparison on two sets of label review records (reviewed_records, changed_labels), returning 2 independent analytical reports for up to 200 rows each.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied label review records (reviewed_records, changed_labels). 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 2 independent reports: (1) a string normalisation report covering the supplied label review records and (2) a keyed dataset comparison report contrasting the two supplied datasets row-by-row on reviewed_records and changed_labels, grouped by dataset_group and date. No external lookups or predictions are made — all analysis is performed locally on the supplied data.
Example use cases
Detecting label drift across annotation batches
“I have two exports from our annotation pipeline — both with row_id, dataset_group, date, reviewed_records, and changed_labels — can you run a string normalisation and keyed comparison on them to find where labels drifted between batch A and batch B?”
Auditing ML training dataset label changes
“We did a label review last week and I have before and after records for about 120 rows, each tagged with row_id, dataset_group, and date. Can you compare the two sets and tell me which rows had changed_labels and whether any normalisation issues exist?”
Reconciling two annotator team exports
“Our two annotation teams submitted separate label review CSVs — I've got up to 80 rows each with row_id, dataset_group, date, reviewed_records, and changed_labels. Please normalise the strings and run a keyed comparison so I can see where their outputs disagree.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-036?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have two structured label review datasets (up to 200 rows each) and need both string normalisation and a keyed record-by-record comparison in a single low-cost call ($0.01 USDC). It is best suited for ML/data labelling QA workflows where you want offline, deterministic analysis without any live lookup, external model inference, or professional assessment. Prefer alternatives if you need real-time label prediction, external data enrichment, or datasets exceeding 200 rows.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — schema rejection
- Empty rows array (minItems: 1 not met) — validation error
- Mismatched row_ids between primary and comparison datasets may produce incomplete keyed comparison
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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