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Data Freshness Analytics — String Normalisation + IQR Outlier Flags
x402
Data Freshness Analytics — String Normalisation + IQR Outlier Flags 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).
Normalises string fields and flags IQR-based outliers across up to 200 data freshness records (age_minutes or record_count), returning two independent analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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
Two independent analytical reports per call covering: (1) string normalisation results for the supplied rows, and (2) IQR-based outlier flags for the chosen numeric field (age_minutes or record_count), applied across up to 200 input rows grouped by dataset_group and date.
Example use cases
Data pipeline freshness audit
“I have 80 rows of data freshness logs with row IDs, dataset groups, dates, age_minutes and record counts — can you normalise the string fields and flag any IQR outliers on the age_minutes column so I can see which pipeline feeds are behaving abnormally?”
Anomalous record count detection
“Run IQR outlier detection and string normalisation on these 120 data freshness records — I want to analyse the record_count field and get two reports highlighting which dataset groups have unusually high or low record counts.”
Pre-reporting data quality check
“Before I send this data freshness summary to my team, can you clean up the string fields and run IQR-based outlier flags on age_minutes for all 35 rows? I need two independent analytical reports to include in my quality review.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined string normalisation pass and IQR-based outlier detection on data freshness metrics (age_minutes or record_count) for up to 200 rows, all in a single $0.01 call returning two reports. Prefer it over general-purpose stats tools when your data is structured around dataset_group and date freshness records and you want offline, local analytical processing with no external lookups or predictions.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause row-level rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values in the selected numeric field may reduce outlier detection accuracy
- Invalid field enum value (not age_minutes or record_count) causes request failure
- Empty rows array returns error — minimum 1 row required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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