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Sampling Observations — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Sampling Observations — 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 2026-10-15).
Runs three local analytical reports on up to 200 sampling observation rows: a record quality audit, string normalisation, and IQR-based outlier flagging for sample_value or sample_weight fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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 bundled reports for up to 200 input rows: (1) a record quality audit identifying missing, malformed, or invalid field values; (2) string normalisation output cleaning dataset_group and other string fields; and (3) IQR outlier flags marking rows whose sample_value or sample_weight falls outside interquartile bounds. All processing is local — no external lookups or predictions are made.
Example use cases
Survey data quality pre-check
“I have 120 rows of survey sampling data with sample_value and sample_weight fields — can you run a record quality audit on them, normalise any messy string fields, and flag any IQR outliers in sample_value before I pass this to my analyst?”
Outlier detection in weighted samples
“Here are 80 sampling observations across several dataset groups. Please flag which rows have IQR outliers in the sample_weight column and give me the quality audit and string normalisation reports too.”
Pre-analysis dataset validation
“Before I run my weighted average calculations, can you check these 200 sampling observation records for data quality issues, clean up the string fields, and identify any sample_value outliers using IQR?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 sampling observation rows with sample_value and sample_weight fields and need all three of: record quality auditing, string normalisation, and IQR outlier detection in a single bundled call at low cost ($0.01 USDC). Prefer it over separate data-cleaning tools when your data is already structured in the expected schema and you want local, deterministic analytics without external dependencies or live lookups.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, sample_value, sample_weight) causes row-level rejection
- Dates not matching YYYY-MM-DD pattern are flagged or rejected
- Values outside ±1,000,000,000,000 range are rejected by schema
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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