# Copyediting Throughput — String Normalisation + Date by Category Pivot

> Copyediting Throughput — String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-05).

Runs two local analytical reports on copyediting throughput data: string normalisation and a date-by-category pivot, for up to 200 rows of edited_words/editing_hours records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-writing-13-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-05
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/copyediting-throughput-string-normalisation-date-by-category-pivot-1837fe55
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6X1Hv5i3ZLDyN7jcoYUlT

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 copyediting-throughput-string-normalisation-date-by-category-pivot-1837fe55 -d '<json body>'
```

Example prompt: Can you run string normalisation and a date-by-category pivot on my copyediting throughput records? I have 4 rows with document groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and I want to analyse the edited_words field.

## When to prefer this

Choose this endpoint when you need two specific analytical operations — string normalisation and date-by-category pivot — applied simultaneously to copyediting throughput records (edited_words, editing_hours) for up to 200 rows, at a flat $0.01 USDC for both reports. It is a purely local computation with no external data access, making it fast, deterministic, and privacy-safe for internal editorial data.

## Known failure modes

- More than 200 rows submitted — request exceeds row limit
- Missing required fields (row_id, document_group, date, edited_words, editing_hours) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- field enum value not 'edited_words' or 'editing_hours' causes rejection
- Null numeric values may reduce pivot completeness
- Empty rows array rejected by minItems constraint

## How this service works

Copyediting throughput — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied copyediting throughput records (edited_words, editing_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 two analytical reports: (1) a string-normalised view of the copyediting throughput records, and (2) a date-by-category pivot table aggregating the selected metric (edited_words or editing_hours) by document_group across dates. Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/copyediting-throughput-string-normalisation-date-by-category-pivot-1837fe55/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)