# Proposal Preparation — Date by Category Pivot + Explicit Missing-Value Fill

> Proposal Preparation — Date by Category Pivot + Explicit 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 2026-10-26).

Pivots proposal preparation data by date and document group, then fills missing values in completed_sections or planned_sections using zero, mean, or median imputation.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-writing-08-189?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/proposal-preparation-date-by-category-pivot-explicit-missing-value-fill-c5b493c8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_sJFzVDuwsPovByOtdCOqw

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-date-by-category-pivot-explicit-missing-value-fill-c5b493c8 -d '<json body>'
```

Example prompt: Take my proposal preparation records — up to 200 rows with row_id, document_group, date, completed_sections, and planned_sections — and run a date-by-category pivot on the completed_sections field, filling any missing values using median imputation, then give me both analytical reports.

## When to prefer this

Choose this endpoint when you have structured proposal preparation records with date, document group, completed_sections, and planned_sections, and need both a pivot table view and clean missing-value imputation in a single low-cost call. Ideal when you need exactly two reports from the same dataset without external data lookup or prediction. Best for datasets up to 200 rows where local analytical computation is sufficient.

## Known failure modes

- Fewer than 1 row or more than 200 rows provided — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Field value out of range (beyond ±1,000,000,000,000) — schema violation
- Unknown fill method or field enum value — invalid input error
- All values missing for a group — mean/median imputation may produce NaN or fallback to zero
- Payment not fulfilled — HTTP 402 returned

## How this service works

Proposal preparation — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill 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

Two analytical reports: (1) a date-by-category pivot table showing the selected field (completed_sections or planned_sections) broken down by date and document_group, and (2) the same dataset with all missing values explicitly filled using the chosen method (zero, mean, or median). Handles up to 200 rows per call.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/proposal-preparation-date-by-category-pivot-explicit-missing-value-fill-c5b493c8/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)