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Validator Uptime Record Quality Audit, String Normalisation & Adjacent Row Changes
x402
Validator Uptime Record Quality Audit, String Normalisation & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Runs three analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 rows of validator uptime data (online_minutes, observed_minutes) using local analytical engines with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Validator uptime — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied validator uptime records (online_minutes, observed_minutes). 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: (1) a record quality audit flagging missing values, out-of-range numbers, or malformed fields; (2) a string normalisation report identifying inconsistencies in text fields like instrument_group; (3) an adjacent row changes report showing differences between consecutive rows for the selected field (online_minutes or observed_minutes). All computed locally from supplied data, no external data fetched.
Example use cases
Validator uptime data quality check
“I have a CSV of 150 validator uptime rows with dates, instrument groups, online_minutes and observed_minutes. Can you run all three reports — record quality audit, string normalisation, and adjacent row changes for online_minutes — to find any data problems before I submit this to my team?”
Detect sudden drops in observed uptime
“Run the adjacent row changes report on these 80 rows of validator uptime data focusing on observed_minutes — I need to know where the biggest jumps or drops between consecutive rows are.”
Clean up instrument group string inconsistencies
“Some of my validator uptime records have messy instrument_group labels with inconsistent capitalisation and spacing. Can you run the string normalisation report on these 60 rows so I can see what needs to be fixed?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-12-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous data quality checks (audit, normalisation, and adjacent row diff) on validator uptime records in a single call at low cost ($0.01 USDC for up to 200 rows). It is ideal for batch offline analysis of time-series uptime data where no live external data or predictions are needed. Prefer it over building custom data-cleaning scripts when working with blockchain validator uptime logs that need quick sanity-checking before downstream use.
Side effects
- Rows array empty or exceeds 200 items — validation error returned
- Required fields (row_id, instrument_group, date, online_minutes, observed_minutes) missing from any row — row-level error flagged
- Date string not matching YYYY-MM-DD pattern — validation rejection
- Field enum value not one of online_minutes or observed_minutes — schema validation error
- Payment not included or insufficient — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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