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Proposal Preparation Record Quality Audit + IQR Outlier Flags
x402
Proposal Preparation Record Quality Audit + IQR Outlier Flags 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 two local analytical reports on proposal preparation records: a record quality audit and an IQR-based outlier detection for completed_sections or planned_sections values, across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Proposal preparation — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied proposal preparation records (completed_sections, planned_sections). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a record quality audit identifying data issues such as missing values, format errors, or inconsistencies across the supplied proposal rows; and (2) IQR-based outlier flags identifying which rows have statistically anomalous values in the selected field (completed_sections or planned_sections). No external data is fetched; all analysis uses local engines on the submitted rows.
Example use cases
Proposal tracker data cleanse
“I've got a spreadsheet of proposal section completion data across multiple document groups — can you run a quality audit and flag any IQR outliers in completed_sections so I can clean up the bad rows before reporting?”
Outlier detection in planned sections
“Check these 50 proposal records for outliers in the planned_sections field using IQR — I suspect a few entries have wildly off counts that are skewing our averages. Here are the rows with row_id, document_group, date, completed_sections, and planned_sections.”
Pre-submission proposal data validation
“Before I finalize our quarterly proposal report, can you audit this batch of proposal preparation records for data quality issues and highlight any rows where completed_sections looks like an IQR outlier?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-08-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality audit plus statistical outlier detection (IQR method) specifically for proposal preparation records containing completed_sections and planned_sections metrics. It is ideal for pre-reporting data cleansing, batch validation of proposal tracking exports, or catching anomalous section counts before analysis. It does not make predictions, perform live lookups, or provide professional assessments — use it for pure local analytical validation of supplied data.
Side effects
- Fewer than 1 row submitted — returns validation error
- More than 200 rows submitted — rejected with payload size error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, document_group, date) — request rejected
- Invalid field enum value — rejected with enum constraint error
- All values null in selected field — IQR outlier report may be empty or indicate insufficient data
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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