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Sampling Observations: String Normalisation + Keyed Dataset Comparison
x402
Sampling Observations: 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 2026-10-24).
Runs string normalisation and keyed dataset comparison analytics on up to 200 sampling observation rows (sample_value, sample_weight) across two supplied datasets, returning 2 local reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied sampling observations records (sample_value, sample_weight). 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 normalisation report covering the submitted sampling observation rows, and (2) a keyed dataset comparison report contrasting the 'rows' and 'other' datasets by row_id and dataset_group. No external lookups or predictions are made; all analysis is performed locally on the supplied data.
Example use cases
A/B sample dataset drift check
“I ran the same sampling process twice and got slightly different numbers — can you compare these two datasets of sampling observations, each tagged with group IDs, dates, sample values, and weights, and tell me where they differ using string normalisation and keyed comparison?”
Weighted sample group consistency audit
“I have two versions of my sampling dataset — rows 1 through 150 each with a dataset_group, date, sample_value, and sample_weight — please normalize the strings and run a keyed comparison so I can see which groups are inconsistent between the two.”
Pre-merge sampling record validation
“Before I merge these two sampling record exports, can you normalise the string fields and compare them key-by-key so I can catch any mismatches in sample values or weights across dataset groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-036?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compare two keyed datasets of weighted sampling observations and also want string normalisation applied — all in a single low-cost call ($0.01 USDC) without any external data lookup or prediction. Prefer this over general-purpose analytics tools when your data fits the sample_value/sample_weight schema with dataset_group keys and you need both normalisation and comparison reports together.
Side effects
- Exceeding 200 rows in either dataset returns a validation error
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause row rejection
- Invalid date format (non-YYYY-MM-DD) fails schema validation
- sample_value or sample_weight outside ±1,000,000,000,000 range causes rejection
- Submitting fewer than 1 row fails minItems validation
- Mismatched row_ids between datasets may result in unmatched comparison entries
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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