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Structured Data Observations — Record Quality Audit + String Normalisation + Daily Sum Series
x402
Structured Data Observations — Record Quality Audit + String Normalisation + Daily Sum Series 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).
Runs three local analytical reports on structured observations data: record quality audit, string normalisation, and daily sum series — up to 200 rows, for valid_items or total_items.
Price / call$0.01
Success—
StatusUnknown
How this service works
Structured data observations — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series 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
Three independent analytical reports based on the supplied rows: (1) a record quality audit flagging issues like missing values or malformed rows, (2) a string normalisation report on text fields like site_group, and (3) a daily sum series aggregating the selected numeric field (valid_items or total_items) by date. No external data is fetched; all analysis is local.
Example use cases
Data quality audit before pipeline load
“I have a batch of 50 structured observation rows with row_id, site_group, date, valid_items, and total_items — can you run a full record quality audit on them and also normalise the string fields and generate a daily sum series for valid_items?”
Daily item count rollup by date
“Take these 30 observation records across multiple site groups and dates and compute the daily sum series for total_items, plus flag any quality issues in the records.”
Spotting anomalies before reporting
“Before I send this dataset to my manager, can you audit these 120 observation rows for record quality problems, normalise any inconsistent strings, and give me a daily sum breakdown for valid_items?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-08-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of structured observation data with date, site_group, valid_items, and total_items fields and need three bundled local reports — quality audit, string normalisation, and daily sums — in a single low-cost call ($0.01 USDC). Prefer it over single-report endpoints when you need all three analyses together without external lookups or predictions.
Side effects
- Input array is empty (minItems:1 violation)
- More than 200 rows submitted (maxItems:200 violation)
- date field does not match YYYY-MM-DD pattern
- row_id is empty or exceeds 80 characters
- field enum value is not 'valid_items' or 'total_items'
- Numeric values outside allowed range (-1e12 to 1e12)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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