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Air Quality Observations — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Air Quality Observations — Record Quality Audit + String Normalisation + Keyed Dataset Comparison 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 local analytical reports on supplied air quality observation records: a record quality audit, string normalisation, and keyed dataset comparison — up to 200 rows, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Air quality observations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied air quality observations records (concentration, observation_count). 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 identifying issues such as nulls, out-of-range values, or malformed fields across the supplied rows; (2) a string normalisation report showing cleaned and standardised versions of text fields like station_group; (3) a keyed dataset comparison report identifying differences between the primary and secondary datasets matched by row_id. All processing is local with no external lookups.
Example use cases
Pre-load air quality data validation
“Before I load this batch of 80 air quality observation rows into our database, can you run a quality audit on them and flag any records with bad strings or nulls in the concentration or observation_count fields? Here are the rows with station_group, date, concentration, and observation_count.”
Reconciling two sensor dataset versions
“I have two versions of my air quality dataset — the original and an updated one, both with up to 120 rows keyed by row_id — can you compare them and tell me which records changed between the two, while also normalising the station_group names?”
Spotting dirty strings in monitoring station labels
“Our air quality monitoring data has messy station_group names with inconsistent casing and spacing. Can you run a string normalisation report on these 60 observation records so I can see what the cleaned values should look like?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-10-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three analytical reports — quality audit, string normalisation, and keyed comparison — on air quality observation data (concentration and observation_count fields) in a single cheap call. Prefer it over generic data validation tools when your data specifically involves air quality monitoring records with station_group labels and you want all three reports bundled for $0.01. Not suitable for live sensor data lookup, forecasting, or professional environmental assessments.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, station_group, date, concentration, observation_count) cause row-level errors
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Rows in 'other' dataset without matching row_ids in 'rows' may show as unmatched in comparison report
- Payment of 0.01 USDC not received results in 402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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