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Data Freshness Analytics: Record Quality Audit + String Normalisation + Category Frequencies
x402
Data Freshness Analytics: Record Quality Audit + String Normalisation + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three local analytical reports — record quality audit, string normalisation, and category frequencies — on up to 200 rows of data freshness records containing age_minutes and record_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied data freshness records (age_minutes, record_count). 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 analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values in age_minutes and record_count; (2) a string normalisation report standardizing dataset_group and other string fields; and (3) a category frequency table showing how often each dataset_group value appears across the submitted rows. All computed locally with no external lookups.
Example use cases
Pipeline freshness quality audit
“I have a batch of freshness records from my ETL pipeline — each row has a row_id, dataset_group, date, age_minutes, and record_count. Can you audit them for record quality issues, normalize the string fields, and give me a category frequency breakdown across all dataset groups?”
Data observability dashboard enrichment
“Before I publish my data freshness dashboard, can you run a quality audit, string normalisation, and category frequency report on these 150 freshness rows so I can catch any nulls, casing inconsistencies, or skewed group distributions?”
Monitoring stale dataset groups
“I'm tracking data freshness across multiple dataset groups and want to know which categories appear most often, whether any age_minutes or record_count values look malformed, and if the group name strings need normalising — can you give me all three reports for these records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, string normalisation, and category frequency analysis on data freshness records in a single low-cost call ($0.01 USDC). It is ideal for ETL monitoring, data observability pipelines, or pre-publish data checks where you have up to 200 rows with age_minutes and record_count fields and want bundled local analytics without any external API calls or live lookups.
Side effects
- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- age_minutes or record_count outside allowed numeric range — error
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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