# Trade Observations String Normalisation and Date by Category Pivot

> Trade Observations String Normalisation and 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 2026-10-26).

Normalises string fields and produces a date-by-category pivot table for trade observation records (export_amount and import_amount) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-economics-06-035?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/trade-observations-string-normalisation-and-date-by-category-pivot-8b5e6eaf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LUcPaavr_LQ6ZMMMkwbKJ

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 trade-observations-string-normalisation-and-date-by-category-pivot-8b5e6eaf -d '<json body>'
```

Example prompt: Can you normalize the region strings and pivot my trade observations by date and category? Here are 4 rows with export_amount and import_amount fields — row-1: 2026-10-01, group-a, export 10, import 1; row-2: 2026-10-02, group-b, export 20, import 2; row-3: 2026-10-03, group-a, export 15, import 3; row-4: 2026-10-04, group-b, export 30, import 4 — and I want the pivot run on export_amount.

## When to prefer this

Choose this endpoint when you need both string normalisation of categorical/region labels AND a date-by-category pivot table in a single $0.01 call for up to 200 trade observation rows. It is purpose-built for trade economics data (export_amount, import_amount) and delivers two complementary reports using shared local engines — no external lookups, live data, or predictions involved. Prefer this over general data tools when your input is structured trade records with region, date, and amount fields.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, region, date, export_amount, import_amount) cause validation errors
- Invalid date format (not YYYY-MM-DD) rejected by pattern constraint
- Numeric values outside ±1,000,000,000,000 range rejected
- Invalid field enum value (not export_amount or import_amount) causes error
- Empty rows array (minItems: 1) rejected

## How this service works

Trade observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied trade observations records (export_amount, import_amount). 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 local analytical reports: (1) a string-normalised version of the region field across supplied rows, and (2) a date-by-category pivot table for the chosen field (export_amount or import_amount), aggregating values across up to 200 trade observation rows grouped by date and region.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/trade-observations-string-normalisation-and-date-by-category-pivot-8b5e6eaf/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)