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Timesheet Imports — Calendar Date Check
x402
Timesheet Imports — Calendar Date Check is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Validates calendar date fields in a timesheet CSV file, checking up to 1000 rows for date format and range correctness.
Price / call$0.01
Success—
StatusUnknown
How this service works
Timesheet imports — Calendar date check: Calendar date check for Timesheet imports. Validate a declared CSV table contract, up to 1000 rows. Quoted cells supported; no external enrichment. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one CSV calendar date check report.
Output
Returns a validation report indicating which rows have calendar date issues, including details on malformed dates, out-of-range values, and any cells exceeding the configured length limit. No external data is fetched; the check is performed entirely on the submitted CSV content.
Example use cases
Pre-payroll timesheet date audit
“Before I run payroll, check this timesheet CSV and flag any rows where the worked_on date is invalid or falls outside January 2026 — delimiter is comma and I expect dates between 2026-01-01 and 2026-01-31.”
Freelancer invoice date validation
“I have a CSV of contractor timesheet entries with a worked_on column — can you validate that all the dates are properly formatted and within Q1 2026 (2026-01-01 to 2026-03-31)? Delimiter is a semicolon.”
Automated timesheet import pipeline check
“As part of my nightly import pipeline, validate the dates in this timesheet CSV — comma-separated, up to 1000 rows — and tell me which rows have calendar date problems so I can reject them before they hit the database.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-csv-timesheets-dates?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, self-contained calendar date validation pass on a timesheet CSV before importing into a payroll or HR system. It is ideal when no external enrichment is needed, you want row-level date error reporting, and your dataset is up to 1000 rows. Prefer it over general-purpose CSV validators when the use case is specifically timesheet worked_on date checking with configurable min/max date ranges.
Side effects
- CSV exceeds 100,000 character limit — request rejected
- More than 1000 rows submitted — may be truncated or rejected
- Invalid delimiter specified — parsing fails
- Malformed CSV structure (unmatched quotes, inconsistent columns) — parsing error returned
- Minimum date greater than maximum date — validation logic error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csv | string | |
| maximum | number | |
| minimum | number | |
| delimiter | string | |
| max_cell_length | integer | |
| allowed_categories | array |
Tags
x402
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