# Account Research Coverage — Base-100 Series + IQR Outlier Flags

> Account Research Coverage — Base-100 Series + IQR Outlier Flags 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).

Generates a Base-100 indexed time series and IQR-based outlier flags for account research coverage data (researched_accounts and target_accounts), producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-164?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-base-100-series-iqr-outlier-flags-7bb0f2ae
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_zEOYqaKoM2XVJ2tijAN18

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-base-100-series-iqr-outlier-flags-7bb0f2ae -d '<json body>'
```

Example prompt: Can you run a base-100 series and IQR outlier analysis on my account research coverage data — I have rows with company_group, date, researched_accounts, and target_accounts for about 50 records across groups A and B, and I want to index off the first row and flag outliers in the researched_accounts field?

## When to prefer this

Choose this endpoint when you have tabular account research coverage data (researched_accounts, target_accounts) organized by company group and date, and need both a base-100 normalization for trend comparison and IQR outlier detection in a single low-cost call. It is ideal for pre-reporting audits, sales ops data quality checks, or any workflow where you want statistical anomaly flags alongside indexed trends without needing live data enrichment, predictions, or external lookups. At $0.01 USDC for two reports on up to 200 rows, it is cost-effective for routine data validation pipelines.

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — validation error
- Missing required fields (row_id, company_group, date, researched_accounts, target_accounts) — schema validation failure
- Invalid date format not matching YYYY-MM-DD pattern — rejected input
- base_index out of range (0–199) relative to supplied rows — index error
- All values null for the selected field — IQR and base-100 computation undefined
- Numeric values outside allowed range (±1,000,000,000,000) — rejected

## How this service works

Account research coverage — Base-100 series + IQR outlier flags: Base-100 series + IQR outlier flags 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

Returns two structured analytical reports: (1) a Base-100 indexed time series showing each row's researched_accounts or target_accounts value relative to a chosen baseline row, and (2) IQR-based outlier flags indicating which rows contain statistically anomalous values. Both reports operate on the same supplied dataset of up to 200 rows and are computed locally without any external data lookup.

## 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-base-100-series-iqr-outlier-flags-7bb0f2ae/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)