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Data Freshness — String Normalisation + Date by Category Pivot
x402
Data Freshness — String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Runs two local analytical reports on data freshness records: string normalisation and a date-by-category pivot for age_minutes or record_count fields, across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied data freshness records (age_minutes, record_count). 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 independent reports: (1) a string normalisation report over the supplied row data, and (2) a date-by-category pivot table aggregating the selected numeric field (age_minutes or record_count) broken down by date and dataset_group — all computed locally with no external lookups or predictions.
Example use cases
Pipeline freshness audit by team
“I have 150 data freshness rows from our pipeline monitoring — each has a date, a dataset_group like 'finance' or 'marketing', and an age_minutes value. Can you normalize the string fields and give me a pivot of age_minutes by date and category so I can see which groups are going stale?”
Record count trend by dataset group
“Here are 80 freshness records with record_count and dataset_group columns. Run string normalisation on the group labels and pivot record_count by date and category so I can spot which groups are losing records over time.”
Data quality check before ingestion
“Before I ingest this batch, can you normalize the string values and produce a date-by-category pivot for age_minutes across my 60 freshness rows? Groups are things like 'raw-events' and 'processed-events' with dates in October 2026.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two bundled local analytical operations — string normalisation and a date-by-category pivot — on data freshness records (age_minutes, record_count) in a single low-cost call. Ideal for pipeline monitoring, ETL quality checks, and freshness audits where no external data, live lookups, or predictions are needed. Prefer over general analytics tools when you want a cheap, deterministic, offline computation on up to 200 freshness rows.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Field selector not one of 'age_minutes' or 'record_count' causes enum validation failure
- Null numeric values may affect pivot aggregation results
- Payment of 0.01 USDC not included or rejected causes 402 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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