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Humidity Observations Data Quality Audit, String Normalisation & Category Frequencies
x402
Humidity Observations Data Quality Audit, String Normalisation & Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Runs three analytical reports on supplied humidity observation records: a record quality audit, string normalisation, and category frequency analysis — all offline, up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Humidity observations — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied humidity observations records (humidity_percent, 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
Returns three independent reports for the supplied rows: (1) a record quality audit flagging data issues, (2) normalised string values for text fields such as station_group, and (3) category frequency counts showing how often each distinct category value appears. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Pre-ingestion data quality check
“Before I load this humidity dataset into our data warehouse, can you run a quality audit on these 120 humidity observation rows — check for bad records, normalise the station_group strings, and show me how frequently each category appears?”
Weather station string standardisation
“I've got humidity readings from multiple weather stations and the station_group labels are inconsistently formatted. Can you normalise those strings and also give me a quality audit and category frequency report for all 80 rows?”
Spotting anomalies in observation counts
“Can you do a record quality audit on these 200 humidity observation records, fix up the station group strings, and break down how often each category shows up? I want to catch any bad data before I run my analysis.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-04-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to quickly audit, clean, and summarise humidity observation records without any external data dependencies. It is ideal for pre-processing pipelines where you need all three reports (quality audit, string normalisation, category frequencies) in a single cheap call ($0.01 USDC) on batches up to 200 rows. Prefer alternatives if you need live weather lookups, predictive modelling, or processing more than 200 rows at once.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, station_group, date, humidity_percent, observation_count) cause row-level rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Empty rows array returns an error
- Null numeric values may be flagged in the quality audit rather than causing failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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