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Structured Data Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes
x402
Structured Data Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Runs three analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 rows of structured observations data containing valid_items and total_items fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Structured data observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied structured data observations records (valid_items, total_items). 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 for the supplied rows: (1) a record quality audit flagging nulls, missing fields, and anomalies; (2) a string normalisation report identifying inconsistencies in text fields like site_group; and (3) an adjacent row changes report showing value differences between consecutive rows for the selected field (valid_items or total_items). All analysis is performed locally on the supplied data — no external lookups or predictions.
Example use cases
Observations dataset quality check
“I've got about 50 rows of daily observations from our monitoring system with site_group, date, valid_items, and total_items columns. Can you run a record quality audit, string normalisation, and adjacent row changes report on them so I can spot any dirty records or inconsistencies?”
Detecting day-over-day shifts in metrics
“We track valid_items and total_items across multiple site groups each day — can you analyse these 80 observation rows and give me the adjacent row changes report focused on valid_items so I can see where big day-to-day jumps are happening?”
Cleaning site group labels in observations
“Our data team suspects site_group names in our observations table are inconsistently formatted. Can you run string normalisation plus a quality audit across these 30 rows to surface any casing or whitespace issues?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-08-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three complementary data-quality reports (quality audit, string normalisation, and adjacent row changes) on structured observation records in a single call. It is ideal for datasets up to 200 rows where all analysis should be performed locally on supplied data without any external lookups, predictions, or live data access. It is especially useful when your records include both numeric metrics (valid_items, total_items) and categorical text fields (site_group) that may need cleaning.
Side effects
- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, site_group, date, valid_items, total_items) in one or more row objects
- Date field does not match YYYY-MM-DD pattern — validation error
- Field selector not one of the allowed enum values (valid_items, total_items)
- Payment not included or insufficient — x402 payment required at $0.01 USDC
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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