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Reservoir Observations Data Quality Audit — Record Quality, String Normalisation & IQR Outlier Flags
x402
Reservoir Observations Data Quality Audit — Record Quality, 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 2026-10-24).
Runs three analytical reports on reservoir observation records (stored_m3, capacity_m3): record quality audit, string normalisation, and IQR-based outlier detection — no external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Reservoir observations — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied reservoir observations records (stored_m3, capacity_m3). 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 structured analytical reports: (1) a record quality audit identifying missing values, malformed dates, and field-level issues; (2) a string normalisation report for station_group and other text fields; (3) an IQR outlier flag report identifying statistically anomalous stored_m3 or capacity_m3 values across the submitted rows.
Example use cases
Water authority data quality review
“I have 120 rows of daily reservoir observations from multiple station groups with stored_m3 and capacity_m3 values — can you audit the record quality, normalise the station group strings, and flag any IQR outliers in stored_m3 so I can clean the dataset before reporting?”
Detecting anomalous capacity readings
“Run an IQR outlier analysis and record quality audit on these 80 reservoir capacity records — I need to know which capacity_m3 values look statistically suspicious and whether any rows have data quality issues like nulls or malformed dates.”
Pipeline pre-processing validation
“Before I load this reservoir dataset into our database, can you check all 200 rows for string normalisation issues in station_group, flag any outliers in stored_m3 using IQR, and give me a record quality report so I know what needs fixing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-19-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have reservoir-specific observation data (stored_m3, capacity_m3) and need all three quality checks — record audit, string normalisation, and IQR outlier detection — in a single call for up to 200 rows. Prefer it over generic data quality tools when your data specifically models reservoir storage and capacity by station group and date, and you want a combined multi-report output without external lookups or predictions.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, station_group, stored_m3, capacity_m3) cause row-level rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 are rejected by schema
- Empty rows array returns a schema validation error
- Selecting an invalid field enum value causes a request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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