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Project Effort Record Quality Audit + String Normalisation + Numeric Interval Filter
x402
Project Effort Record Quality Audit + String Normalisation + Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Runs three local analytical reports on project effort data (estimated_hours, actual_hours): a record quality audit, string normalisation, and numeric interval filter — up to 200 rows, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project effort — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter for supplied project effort records (estimated_hours, actual_hours). 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 issues in the supplied rows, (2) a string normalisation report for text fields, and (3) a numeric interval filter report identifying rows where the selected field (estimated_hours or actual_hours) falls within or outside the specified minimum–maximum range. All analysis is performed locally with no external data sources.
Example use cases
Pre-import effort data quality check
“Before I load this sprint effort data into our project tracker, can you run a quality audit on these 150 rows — each has a row_id, team_group, date, estimated_hours, and actual_hours — and also normalise the team_group strings and filter out any rows where estimated_hours falls outside 0 to 80?”
Detecting out-of-range hours in contractor timesheets
“I have 80 contractor timesheet rows with estimated and actual hours and I need to know which ones have actual_hours outside the interval 0 to 500 — also run a record quality audit and string normalisation on the team_group field while you're at it.”
Cleaning effort records before analytics pipeline
“Can you clean up these 200 project effort records for me? Run the quality audit to catch bad rows, normalise the team_group strings, and filter estimated_hours to only those between 1 and 40.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-operations-01-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations on project effort data in a single cheap call ($0.01 USDC): quality auditing of records, string normalisation of team/group labels, and numeric range filtering on hours fields. It is ideal for pre-processing or validating effort data before loading into downstream systems. Prefer it over separate tools when all three operations are needed together on the same dataset of up to 200 rows, and no live lookups or predictions are required.
Side effects
- More than 200 rows submitted — request rejected
- Missing required fields (row_id, team_group, date, estimated_hours, actual_hours) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema rejection
- minimum greater than maximum — may produce empty interval filter results
- Non-numeric values in estimated_hours or actual_hours — schema validation failure
- Empty rows array — schema rejection (minItems: 1)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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