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Sampling Observations — Record Quality Audit + String Normalisation + Grouped Numeric Summary
x402
Sampling Observations — Record Quality Audit + String Normalisation + Grouped Numeric Summary 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 three local analytical reports on sampling observation data (record quality audit, string normalisation, and grouped numeric summary) for up to 200 rows with sample_value and sample_weight fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary for supplied sampling observations records (sample_value, sample_weight). 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 structured analytical reports: (1) a record quality audit flagging missing, malformed, or out-of-range values across the supplied rows; (2) a string normalisation report cleaning and standardising text fields like dataset_group; (3) a grouped numeric summary aggregating the selected numeric field (sample_value or sample_weight) by dataset_group — all computed locally with no external data lookups.
Example use cases
Survey data quality check
“I have 150 rows of survey sampling data with row IDs, group labels, dates, sample values, and weights. Can you audit them for record quality issues, normalise any string fields, and give me a grouped numeric summary of sample_value by dataset_group?”
Weighted sample aggregation by group
“Please run a grouped numeric summary on my sampling observations — I've got about 80 rows across groups A, B, and C with sample_weight values — and also flag any data quality problems or inconsistent strings in the dataset.”
Sampling dataset pre-analysis validation
“Before I run my analysis, can you check these 200 sampling observation records for completeness and quality issues, normalise the string fields, and show me how sample_weight breaks down across each dataset group?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous local analytical reports — record quality audit, string normalisation, and grouped numeric summary — on sampling observation data (with sample_value and sample_weight columns) for up to 200 rows, all in a single low-cost call. Prefer it over single-report endpoints when you need all three reports together for efficiency. Not suitable for real-time lookups, predictions, external data enrichment, or datasets exceeding 200 rows.
Side effects
- Exceeding 200 rows causes a validation error
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) result in schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside the allowed range (±1,000,000,000,000) are rejected
- Empty rows array is rejected (minItems: 1)
- Invalid field enum value (not sample_value or sample_weight) causes an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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