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Sampling Observations Analyzer — Record Quality Audit + String Normalisation + Category Frequencies
x402
Sampling Observations Analyzer — Record Quality Audit + String Normalisation + Category Frequencies 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 (record quality audit, string normalisation, and category frequencies) on up to 200 sampling observation 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 + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 independent analytical reports: (1) a record quality audit flagging missing, malformed, or out-of-range values; (2) a string normalisation report standardising text fields like dataset_group; and (3) a category frequency report counting occurrences of each distinct category value across the supplied rows.
Example use cases
Survey data quality check
“I have a batch of 120 survey sampling records with row IDs, dates, group labels, sample values, and sample weights. Can you run a full quality audit on them, normalise the group name strings, and tell me the frequency of each category?”
Weighted sample category analysis
“I've collected 80 weighted sampling observations across different dataset groups and I need to know the category frequency breakdown, catch any string inconsistencies in the group labels, and flag any quality issues in the records.”
Pre-analysis dataset cleanup
“Before I run my statistical model, can you audit these 200 sampling observation rows for record quality issues, normalise the string fields in the dataset_group column, and give me a count of how frequently each category appears?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, string normalisation, and category frequency analysis run together on sampling observation data (sample_value, sample_weight) in a single low-cost call. Ideal for datasets up to 200 rows where you want offline, local analytical reports without any external lookups, live predictions, or professional assessments.
Side effects
- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause validation errors
- Dates not matching YYYY-MM-DD pattern are rejected
- sample_value or sample_weight outside the allowed numeric range causes errors
- Empty rows array (minItems: 1 not met) results in validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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