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Issue Throughput Data Quality Bundle: Audit + String Normalisation + Missing-Value Fill
x402
Issue Throughput Data Quality Bundle: Audit + String Normalisation + Missing-Value Fill 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 data-quality reports on issue throughput records (issues_opened, issues_closed): a record quality audit, string normalisation, and explicit missing-value fill — all processed locally with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Issue throughput — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied issue throughput records (issues_opened, issues_closed). 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 data-quality reports covering: (1) a record quality audit flagging malformed, inconsistent, or invalid rows; (2) normalised string values for text fields such as component names; and (3) explicit imputed values replacing nulls in the target numeric field (issues_opened or issues_closed) using the chosen fill method (zero, mean, or median). All reports are generated locally from the supplied data with no external lookups.
Example use cases
Pre-analysis data cleaning for sprint metrics
“I have 120 rows of daily issue throughput data with some nulls in issues_closed and inconsistent component name casing — can you run a full quality audit, normalise the strings, and fill those missing values using the mean before I analyse trends?”
Validating imported issue data from legacy system
“We just imported issue records from our old tracker and I suspect there are quality problems — can you audit up to 200 of these rows for record quality issues, normalise the component strings, and fill any missing issues_opened counts with zero?”
Routine data quality check for engineering dashboard
“Can you run the three-report data quality bundle on this week's issue throughput rows — record audit, string normalisation, and median fill for issues_closed — so our dashboard doesn't break on bad data?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-software-13-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC for three reports) data-quality pass on issue throughput records without requiring any live external data, predictions, or professional assessment. It is ideal for pre-processing software engineering metrics (issues opened/closed) before downstream analysis or dashboarding, especially when you need record auditing, string normalisation, and missing-value imputation in a single call for up to 200 rows.
Side effects
- Exceeding 200-row limit returns a validation error
- Missing required fields (row_id, component, date, issues_opened, issues_closed) cause row-level errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Unsupported fill method enum value returns schema validation error
- Empty rows array rejected by minItems:1 constraint
- Payment failure via x402 prevents processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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