# Account Research Coverage — Record Quality Audit + String Normalisation + Trailing Mean Series

> Account Research Coverage — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Runs three local analytical reports (record quality audit, string normalisation, and trailing mean series) on account research coverage data (researched_accounts, target_accounts) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-199?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/account-research-coverage-record-quality-audit-string-normalisation-da89e049
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1bM3928n9FOAcXI2Dg9P5

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-da89e049 -d '<json body>'
```

Example prompt: Can you audit the quality of my account research coverage records, normalise the company_group strings, and compute a trailing mean over a window of 3 for researched_accounts? Here are my rows: [{"row_id":"r1","company_group":"group-a","date":"2025-01-01","researched_accounts":12,"target_accounts":50},{"row_id":"r2","company_group":"Group-A","date":"2025-01-02","researched_accounts":15,"target_accounts":null}]

## When to prefer this

Choose this endpoint when you need three bundled analytical operations — quality audit, string normalisation, and trailing mean series — on account research coverage data in a single low-cost call ($0.01 USDC). Ideal for sales ops teams, revenue analysts, or data pipelines that need to clean and summarise researched_accounts or target_accounts datasets of up to 200 rows without any external data enrichment or live lookups. Prefer this over separate ETL tools when you want a fast, self-contained, offline analysis bundle.

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — request rejected
- date field not matching YYYY-MM-DD pattern — validation error
- numeric values for researched_accounts or target_accounts outside allowed range — schema error
- window value less than 2 or greater than 200 — parameter error
- Missing required fields (row_id, company_group, date) — schema validation failure
- Payment not completed or insufficient USDC — 402 payment required

## How this service works

Account research coverage — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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 three independent analytical reports: (1) a record quality audit flagging completeness, validity, and formatting issues per row; (2) a string normalisation report standardising company_group and other text fields; and (3) a trailing mean series for the specified numeric field (researched_accounts or target_accounts) computed over the given window size — all derived 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",
     "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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-record-quality-audit-string-normalisation-da89e049/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)