# Account Research Coverage — Grouped Numeric Summary + IQR Outlier Flags

> Account Research Coverage — Grouped Numeric Summary + 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).

Computes grouped numeric summary statistics and IQR-based outlier flags for account research coverage data (researched_accounts, target_accounts) across company groups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-094?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-grouped-numeric-summary-iqr-outlier-flags-00f11b05
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_b9Y-ppzSEjKmkTGAteQmO

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-grouped-numeric-summary-iqr-outlier-flags-00f11b05 -d '<json body>'
```

Example prompt: Can you run a grouped numeric summary and flag IQR outliers on my account research coverage data — I have rows with company_group, date, researched_accounts, and target_accounts fields, up to 200 rows — and analyze the researched_accounts field?

## When to prefer this

Choose this endpoint when you have raw account research coverage records (researched_accounts, target_accounts) organized by company group and date, and need both grouped descriptive statistics and IQR-based outlier detection in a single call without any external data lookup or live enrichment. It is well-suited for sales ops teams auditing pipeline coverage, analysts benchmarking research activity across business units, or automated pipelines flagging anomalous account data before reporting. Prefer this over generic analytics tools when you want a low-cost ($0.01 USDC), self-contained, on-supplied-data computation with no side effects.

## Known failure modes

- Fewer than 1 row provided — request rejected
- More than 200 rows submitted — exceeds maximum row limit
- Missing required fields (row_id, company_group, date, researched_accounts, target_accounts) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- field enum value not one of 'researched_accounts' or 'target_accounts' — rejected
- All values null for selected field — summary may be empty or undefined
- Payment of 0.01 USDC not provided — x402 payment required error

## How this service works

Account research coverage — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + 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 independent analytical reports: (1) a grouped numeric summary (e.g. mean, median, min, max, quartiles, std dev) broken down by company_group for the selected field, and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range bounds, for up to 200 supplied rows across researched_accounts or target_accounts.

## 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-grouped-numeric-summary-iqr-outlier-flags-00f11b05/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)