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Sampling Observations — Record Quality Audit + IQR Outlier Flags
x402
Sampling Observations — Record Quality Audit + 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 2026-10-15).
Runs a record quality audit and IQR-based outlier detection on up to 200 rows of sampling observation data (sample_value, sample_weight), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied sampling observations records (sample_value, sample_weight). 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: (1) a record quality audit identifying malformed, missing, or invalid rows among the supplied sampling observations, and (2) IQR-based outlier flags indicating which rows contain statistically extreme values in the selected field (sample_value or sample_weight). No external lookups or predictions are made — analysis is performed entirely on the supplied data.
Example use cases
Pre-analysis QC on survey samples
“I have 150 rows of survey sampling data with sample values and weights collected across two groups in October 2026 — can you audit the record quality and flag any IQR outliers in the sample_value field before I run my analysis?”
Detecting bad weights in lab samples
“Run an IQR outlier check on the sample_weight column for my lab sampling observations — I have about 80 rows with row IDs, dates, and group labels, and I need to know which rows have anomalous weights.”
Data validation before pipeline ingestion
“Before I ingest this batch of 200 sampling records into our pipeline, can you run a full record quality audit and outlier flag report on the sample_value column so I can catch any bad data first?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a structural record quality check and IQR-based statistical outlier detection on sampling observation data in a single call. It is well-suited for pre-processing validation steps before downstream analysis, reporting, or pipeline ingestion. Prefer it over single-purpose validators when you want two independent reports (quality audit + outlier flags) on the same dataset without making separate API calls. It is purely local/analytical — no external data sources or live lookups are involved.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause row-level validation errors
- Invalid date format (not YYYY-MM-DD) may cause parsing failures
- Null values in the selected analysis field may affect IQR calculation
- Insufficient data rows for meaningful IQR computation (e.g. only 1-2 rows)
- Payment not processed — 402 response if USDC payment is not attached
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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