# Account Research Coverage — Record Quality Audit, String Normalisation & Missing-Value Fill

> Account Research Coverage — Record Quality Audit, String Normalisation & 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 2026-10-15).

Audits data quality, normalises strings, and fills missing values in account research coverage records (researched_accounts, target_accounts) for up to 200 rows, returning 3 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-208?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-record-quality-audit-string-normalisation-8271071c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_BPyKKePkUXNhPDqbbMJQJ

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-record-quality-audit-string-normalisation-8271071c -d '<json body>'
```

Example prompt: Can you audit the data quality, normalise the company group strings, and fill missing researched_accounts values using median imputation for these 4 coverage rows: row-1 group-a 2026-10-01 researched=10 target=1, row-2 group-b 2026-10-02 researched=null target=2, row-3 group-a 2026-10-03 researched=15 target=3, row-4 group-b 2026-10-04 researched=30 target=null?

## When to prefer this

Choose this endpoint when you need a fast, offline, local-engine data quality pass on account research coverage records — specifically when you want all three of record quality auditing, string normalisation, and missing-value imputation in a single low-cost call. Ideal for pre-ingestion validation, weekly data health checks, or preparing CRM coverage data for reporting. Not appropriate when you need live data enrichment, external company lookups, predictive modelling, or processing more than 200 rows at once.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — request rejected
- date field does not match YYYY-MM-DD pattern — validation error
- row_id missing or empty — required field error
- numeric values outside allowed range (±1 trillion) — schema rejection
- invalid method enum value (not zero/mean/median) — parameter error
- invalid field enum value (not researched_accounts/target_accounts) — parameter error

## How this service works

Account research coverage — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied account research coverage records (researched_accounts, target_accounts). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Returns 3 independent analytical reports for the supplied rows: (1) a record quality audit flagging malformed, out-of-range, or inconsistent values; (2) a string normalisation report showing standardised company_group and other string fields; and (3) a missing-value fill report with imputed values for the selected field (researched_accounts or target_accounts) using the chosen method (zero, mean, or median). No external data sources or live lookups are used.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-record-quality-audit-string-normalisation-8271071c/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)