# Account Research Coverage: IQR Outlier Flags + Keyed Dataset Comparison

> Account Research Coverage: IQR Outlier Flags + Keyed Dataset Comparison 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 two statistical reports on account research coverage data: IQR-based outlier detection and keyed dataset comparison, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-179?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-iqr-outlier-flags-keyed-dataset-comparison-adb504fa
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ImVR6YmqJuWvB29ssCWoR

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-iqr-outlier-flags-keyed-dataset-comparison-adb504fa -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and a keyed dataset comparison on my account research coverage data? I have two sets of records with row_id, company_group, date, researched_accounts, and target_accounts — up to 50 rows each — and I want to flag the outliers in the researched_accounts field.

## When to prefer this

Choose this endpoint when you need fast, local statistical analysis on account research coverage records — specifically IQR-based outlier detection and keyed dataset comparison — without any external data enrichment or live lookup. Ideal for data quality audits, pre-import validation, or comparing two snapshots of account research data, especially when datasets are under 200 rows and privacy or cost constraints favor local computation over cloud analytics.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, company_group, date, researched_accounts, target_accounts) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Non-numeric values in numeric fields — type error
- Empty rows array — minimum items violation
- Payment not included or insufficient — x402 payment required response

## How this service works

Account research coverage — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + Keyed dataset comparison 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) IQR outlier flags identifying which rows have statistically anomalous values for the selected field, and (2) a keyed dataset comparison showing differences between the supplied rows and other datasets, all computed locally without 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}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "company_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "target_accounts": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "researched_accounts": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "company_group": "group-a",
     "target_accounts": 1,
     "researched_accounts": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "company_group": "group-b",
     "target_accounts": 2,
     "researched_accounts": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "company_group": "group-a",
     "target_accounts": 3,
     "researched_accounts": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "company_group": "group-b",
     "target_accounts": 4,
     "researched_accounts": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "researched_accounts",
    "target_accounts"
   ],
   "type": "string",
   "default": "researched_accounts"
  },
  "other": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "company_group",
     "date",
     "researched_accounts",
     "target_accounts"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "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
     },
   
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-iqr-outlier-flags-keyed-dataset-comparison-adb504fa/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)