# Shot Schedules String Normalisation and Missing-Value Fill

> Shot Schedules String Normalisation and 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 2026-10-22).

Normalises strings and fills missing values in shot schedule records (planned_hours, actual_hours) for up to 200 rows, producing 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-13-037?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/shot-schedules-string-normalisation-and-missing-value-fill-0a8eab61
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_71Mtky_BFNF_hIAKX6BQ8

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 shot-schedules-string-normalisation-and-missing-value-fill-0a8eab61 -d '<json body>'
```

Example prompt: I have a shot schedule with some missing planned_hours values — can you clean up the strings and fill in the blanks using median imputation for the planned_hours field? I have about 50 rows covering asset groups like 'group-a' and 'group-b' dated from 2026-10-01 onwards.

## When to prefer this

Choose this endpoint when you have shot schedule data (planned_hours and actual_hours per asset group and date) that contains nulls or inconsistent string formatting and you need both string normalisation and explicit missing-value imputation (zero, mean, or median) in a single call, with results returned as 2 reports. Ideal for pre-processing film or media production schedules before downstream analytics. Not suitable if you need live data lookup, forecasting, or more than 200 rows per call.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, asset_group) — validation error
- Invalid date format (not YYYY-MM-DD pattern) — schema validation failure
- Unsupported fill method or field enum value — request rejected
- Payment not received or insufficient USDC — x402 payment required error
- Empty rows array — rejected due to minItems:1 constraint

## How this service works

Shot schedules — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied shot schedules records (planned_hours, actual_hours). 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 2 analytical reports covering the submitted shot schedule rows (up to 200): one covering string normalisation results and one covering explicit missing-value fill for the specified field (planned_hours or actual_hours) using the chosen method (zero, mean, or median). No external lookups or predictions are made; all processing is local to the submitted data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/shot-schedules-string-normalisation-and-missing-value-fill-0a8eab61/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)