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Sampling Observations Data Quality Processor
x402
Sampling Observations Data Quality Processor 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).
Audits record quality, normalises strings, and fills missing values for sampling observations datasets (sample_value, sample_weight) — up to 200 rows, 3 reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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 malformed, missing, or out-of-range values per row; (2) a string normalisation report for text fields like dataset_group and date; and (3) a missing-value fill report showing which sample_value or sample_weight cells were null and what value was imputed using the chosen method (zero, mean, or median). All processing is local with no external lookups.
Example use cases
Pre-analysis missing value imputation
“I have 120 sampling observation rows with some null sample_weight values — can you fill those missing weights using mean imputation and give me the quality audit report before I run my weighted analysis?”
Survey data string normalisation check
“My survey sampling dataset has inconsistent text in the dataset_group field across 80 rows — can you run string normalisation and a record quality audit on it so I know what needs fixing?”
Batch data validation before pipeline ingestion
“Before I load these 200 sampling observation records into our data pipeline, can you do a full record quality audit and flag any missing or malformed values in sample_value and sample_weight — use zero fill for any missing values?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined data-quality operations — audit, string normalisation, and missing-value imputation — on sampling observation records in a single $0.01 call. It is ideal for pre-processing tabular survey or sampling datasets before downstream analysis, when you want offline/local processing with no external API dependencies, and when your dataset fits within 200 rows. Prefer alternatives if you need predictive modelling, live data lookup, or operations on non-sampling entity types like business proposals or retail figures.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (non-YYYY-MM-DD) triggers schema rejection
- sample_value or sample_weight outside ±1,000,000,000,000 range fails validation
- Missing required fields (row_id, date, dataset_group) causes row-level rejection
- Invalid method enum value (not zero/mean/median) returns a bad request error
- Invalid field enum value (not sample_value/sample_weight) returns a bad request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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