# Account Research Coverage — String Normalisation + Date by Category Pivot

> Account Research Coverage — 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 2026-10-15).

Normalises string fields and produces a date-by-category pivot report for up to 200 account research coverage rows, delivering two analytical reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/account-research-coverage-string-normalisation-date-by-category-pivot-27813264
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hCL4fK0yrrUo-YcCnCgpW

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 account-research-coverage-string-normalisation-date-by-category-pivot-27813264 -d '<json body>'
```

Example prompt: I have account research coverage records with company groups like 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and columns for researched_accounts and target_accounts — can you normalise the string fields and pivot the researched_accounts data by date and category, up to 200 rows?

## When to prefer this

Choose this endpoint when you have up to 200 account research coverage records and need both string normalisation of categorical fields and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is best suited for offline batch analytics on supplied data rather than live lookups or predictive modelling. Prefer it over general-purpose analytics tools when you need two bundled reports (normalisation + pivot) with a predictable flat price and no external data access.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, company_group, date, researched_accounts, target_accounts) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside allowed range (±1 trillion) — rejected
- Empty rows array — minimum 1 item required

## How this service works

Account research coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied account research coverage records (researched_accounts, target_accounts). 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 string-normalised version of the input records with cleaned company_group and other text fields, and (2) a date-by-category pivot table showing the selected numeric field (researched_accounts or target_accounts) broken down by date and company_group — all computed locally with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-string-normalisation-date-by-category-pivot-27813264/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)