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Dataset Partition Sizes — Record Quality Audit, String Normalisation & IQR Outlier Flags
x402
Dataset Partition Sizes — Record Quality Audit, 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 2026-10-19).
Audits record quality, normalises strings, and flags IQR-based outliers for dataset partition size records (row_count, file_mb) — delivering 3 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset partition sizes — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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 covering: (1) a record quality audit identifying missing, malformed, or invalid fields; (2) string normalisation findings for text fields like dataset_group; and (3) IQR-based outlier flags on the chosen numeric field (row_count or file_mb). All reports are computed locally with no external lookups.
Example use cases
ETL pipeline partition audit
“I've got a list of dataset partitions from last week's ETL runs — each has a row_id, dataset_group, date, row_count, and file_mb. Can you run a quality audit, normalise any messy group name strings, and flag any IQR outliers in the row_count column?”
Detecting oversized file partitions
“Here are 50 partition records from our data lake, each with row counts and file sizes in MB. Can you check them for record quality issues, clean up the dataset_group labels, and highlight any file_mb values that look like outliers using IQR?”
Data pipeline health check for QA review
“Before our QA review, I need a health check on these 120 dataset partition records — run the record quality audit, string normalisation, and IQR outlier flags on the file_mb field so I can spot anything suspicious.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-07-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality pass — record auditing, text normalisation, and statistical outlier detection — on dataset partition metadata (row counts and file sizes) in a single $0.01 call. It is purely analytical with no side effects, making it safe to call repeatedly. Prefer it over general-purpose data quality tools when your records specifically contain row_count and file_mb fields and you want IQR-based outlier flagging alongside string cleanup in one bundled response.
Side effects
- Input array is empty or exceeds 200 rows — rejected with validation error
- Required fields (row_id, date, row_count, file_mb, dataset_group) missing from row objects — schema validation failure
- Date fields not matching YYYY-MM-DD pattern — rejected
- Numeric fields outside allowed range — rejected
- Payment of 0.01 USDC not completed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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