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Sampling Observations Analytics: Record Quality Audit + String Normalisation + Daily Sum Series
x402
Sampling Observations Analytics: Record Quality Audit + String Normalisation + Daily Sum 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 rows — a record quality audit, string normalisation, and daily sum series — for up to 200 rows of sample_value/sample_weight data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum 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 analytical reports returned together: (1) a record quality audit flagging issues in the supplied rows, (2) a string normalisation report cleaning and standardising text fields, and (3) a daily sum series aggregating the chosen numeric field (sample_value or sample_weight) by date. All computed locally with no external lookups.
Example use cases
Audit incoming sensor sample data quality
“I just received a batch of 120 sensor sampling records with row_id, dataset_group, date, sample_value, and sample_weight. Can you run a quality audit on them, normalise any string fields, and give me the daily sum series for sample_value so I can spot anomalies?”
Daily aggregation for weighted survey responses
“I have 80 rows of survey sampling observations — each has a row_id, group label, date, sample_value score, and sample_weight. Please compute the daily sum series on sample_weight and also flag any record quality issues or string inconsistencies.”
Validate and clean environmental monitoring samples
“Before I import these 200 environmental monitoring samples into our database, can you run the record quality audit and string normalisation on them, and also produce the daily sum series for sample_value? Fields are row_id, dataset_group, date, sample_value, and sample_weight.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations — quality auditing, string normalisation, and daily sum aggregation — on a single batch of sampling observation records (up to 200 rows) in one low-cost call, without requiring any live data lookup or external API calls. Ideal for pre-import validation, ETL pipeline checks, or exploratory data quality workflows on weighted sample data.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause validation errors
- Date strings not matching YYYY-MM-DD pattern rejected
- Numeric values outside ±1,000,000,000,000 bounds cause schema errors
- Empty rows array returns error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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