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Sampling Observations — Record Quality Audit + String Normalisation + Base-100 Series
x402
Sampling Observations — Record Quality Audit + String Normalisation + Base-100 Series 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 records: a record quality audit, string normalisation, and a base-100 indexed series — all offline, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 reports: (1) a record quality audit flagging completeness, nulls, and structural issues; (2) a string normalisation report standardising text fields like dataset_group; (3) a base-100 indexed series for the chosen numeric field (sample_value or sample_weight) relative to the specified base row index. All computed locally from the supplied rows, no external data.
Example use cases
Audit incoming survey sample data
“I've got a batch of survey sampling records with row IDs, group labels, dates, sample values, and weights — can you run a full quality audit, string normalisation, and base-100 series on the sample_value field, anchored to the first row?”
Baseline-index experiment readings
“I have up to 150 experiment observation rows with sample_value and sample_weight — please produce a base-100 indexed series for sample_value starting from row index 0, along with the quality audit and string normalisation reports.”
Clean and validate market research samples
“My market research dataset has 80 sampling observation records across several dataset groups — can you run all three reports (record quality audit, string normalisation, and base-100 series on sample_weight anchored at index 0) so I can check data integrity before analysis?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled offline analytics reports — quality audit, string normalisation, and base-100 indexing — on up to 200 sampling observation rows in a single low-cost call. Prefer it over separate single-report endpoints when your data already contains sample_value and sample_weight fields and you want comprehensive data-quality and trend analysis without any external lookups or live data enrichment.
Side effects
- More than 200 rows supplied — request rejected at maxItems limit
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) on any row — validation error
- base_index out of range (≥ number of rows supplied) — index error
- date field not matching YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside ±1,000,000,000,000 range — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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