# Proposal Preparation Record Quality Audit + IQR Outlier Flags

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-writing-08-014?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/proposal-preparation-record-quality-audit-iqr-outlier-flags-849773fe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_u9U1rjAAWfBpQd4pHW-u2

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability proposal-preparation-record-quality-audit-iqr-outlier-flags-849773fe -d '<json body>'
```

Example prompt: Can you audit these proposal preparation records for data quality issues and flag any IQR outliers in the completed_sections field? I have 4 rows: row-1 (group-a, 2026-10-01, completed=10, planned=1), row-2 (group-b, 2026-10-02, completed=20, planned=2), row-3 (group-a, 2026-10-03, completed=15, planned=3), row-4 (group-b, 2026-10-04, completed=30, planned=4).

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "document_group",
     "date",
     "completed_sections",
     "planned_sections"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "document_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "planned_sections": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "completed_sections": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "document_group": "group-a",
     "planned_sections": 1,
     "completed_sections": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "document_group": "group-b",
     "planned_sections": 2,
     "completed_sections": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "document_group": "group-a",
     "planned_sections": 3,
     "completed_sections": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "document_group": "group-b",
     "planned_sections": 4,
     "completed_sections": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "completed_sections",
    "planned_sections"
   ],
   "type": "string",
   "default": "completed_sections"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/proposal-preparation-record-quality-audit-iqr-outlier-flags-849773fe/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)