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Dataset Partition Sizes — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series
x402
Dataset Partition Sizes — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series 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 independent analytical reports (record quality audit, exact row deduplication, and trailing mean series) on dataset partition size records containing row counts and file sizes.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset partition sizes — Record quality audit + Exact row deduplication + Trailing mean series: Record quality audit + Exact row deduplication + Trailing mean series 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
Returns three independent reports: (1) a record quality audit identifying missing values, invalid fields, or anomalies across the supplied rows; (2) an exact row deduplication report listing duplicate records found in the input; and (3) a trailing mean series computed over the specified numeric field (row_count or file_mb) using the given sliding window size. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Partition log quality check
“I have a list of 50 dataset partition records with row_count and file_mb going back a few months — can you audit them for quality issues, flag any duplicate rows, and give me a trailing 7-day mean series on row_count?”
Deduplication before pipeline run
“Before I process my pipeline, I need to deduplicate this set of 120 partition size records and also get a quality audit so I can see which rows have nulls or anomalies — can you run both reports on this data?”
Rolling file size trend analysis
“Can you compute a trailing mean series with a window of 5 on the file_mb column across my dataset partition records, and also run the quality audit and deduplication reports at the same time so I get all three in one shot?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-07-216?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical operations — quality audit, deduplication, and trailing mean — on dataset partition size data (row_count, file_mb) in a single low-cost call. It is ideal for data engineering pipelines that need lightweight, offline analysis of partition logs without hitting external databases or prediction services. Prefer it over single-report endpoints when you need all three reports together for up to 200 rows at $0.01 USDC.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) on any row — validation error
- Invalid date format (must match YYYY-MM-DD) — schema rejection
- Window size less than 2 or greater than 200 — parameter error
- Field enum value not 'row_count' or 'file_mb' — validation failure
- Empty rows array (minItems: 1 not satisfied) — rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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