# Account Research Coverage — String Normalisation + Two-Measure Correlation

> Account Research Coverage — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Normalises strings and computes two-measure correlation analysis on account research coverage data (researched_accounts vs target_accounts) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/account-research-coverage-string-normalisation-two-measure-correlation-03bd6e81
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FLh0IEg7r8iXBJFOusLV6

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-two-measure-correlation-03bd6e81 -d '<json body>'
```

Example prompt: I have account research coverage records for two company groups — group-a and group-b — across four dates in October 2026. Can you normalise the string fields and run a two-measure correlation between researched_accounts and target_accounts for those rows?

## When to prefer this

Choose this endpoint when you need both string normalisation and two-measure correlation computed together on account research coverage data (researched_accounts and target_accounts) in a single low-cost call. It is ideal for sales ops, CRM analytics, or pipeline review workflows where you want to clean string fields and understand the statistical relationship between two numeric coverage metrics across up to 200 rows, without needing live lookups, predictions, or external integrations.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — exceeds batch limit
- Missing required fields (row_id, date, researched_accounts, target_accounts) — schema validation failure
- Date field not matching YYYY-MM-DD pattern — format validation error
- Numeric values outside allowed range (±1,000,000,000,000) — value validation error
- Insufficient numeric variance in one measure — correlation may be undefined or return NaN

## How this service works

Account research coverage — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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: one string normalisation report (cleaned/standardised company_group and other string fields) and one two-measure correlation report quantifying the relationship between researched_accounts and target_accounts across the submitted rows. No external data is fetched; all analysis is performed on the supplied rows only.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-string-normalisation-two-measure-correlation-03bd6e81/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)