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Transformation Throughput — String Normalisation + IQR Outlier Flags
x402
Transformation Throughput — 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 2026-10-28).
Normalises string fields and flags IQR-based outliers for transformation throughput records (processed_rows, duration_seconds) across up to 200 rows, returning 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transformation throughput — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied transformation throughput records (processed_rows, duration_seconds). 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: one covering string normalisation of the dataset_group and related string fields, and one flagging per-row IQR outlier status for the selected numeric field (processed_rows or duration_seconds), including computed IQR thresholds and per-row flags across up to 200 input records.
Example use cases
ETL pipeline anomaly detection
“I have daily ETL job logs for two dataset groups from last week — 8 rows with row IDs, dates, processed_rows, and duration_seconds. Can you normalize the group name strings and flag any outlier jobs by processed_rows using IQR analysis?”
Data warehouse load monitoring
“Here are 20 data warehouse load records with row_id, dataset_group, date, processed_rows, and duration_seconds. Run IQR outlier flagging on duration_seconds and normalize the dataset group labels so I can see which loads took abnormally long.”
Pipeline throughput QA review
“I need to QA our transformation throughput logs before the sprint review — I have 50 rows with processed_rows and duration_seconds. Can you normalize the string fields and flag statistical outliers using IQR on processed_rows so I can spot bad runs?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-13-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalization and IQR-based statistical outlier detection on ETL or data pipeline throughput records in a single call, without requiring external data lookups, ML inference, or live data connections. Ideal for data quality pipelines, pipeline monitoring dashboards, or pre-report data cleaning workflows where processed_rows or duration_seconds anomaly detection is needed on batches up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to maxItems limit
- Missing required fields (row_id, date, processed_rows, duration_seconds) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- All values for selected field are null — IQR computation cannot proceed
- Numeric values outside ±1e12 range rejected by schema bounds
- Malformed JSON payload returns 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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