# Data Reconciliation — Category Frequencies + Two-Measure Correlation

> Data Reconciliation — Category Frequencies + Two-Measure Correlation 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).

Analyzes data reconciliation records to produce category frequency distributions and a two-measure correlation report between source_total and target_total values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-108?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/data-reconciliation-category-frequencies-two-measure-correlation-fdf55b92
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_SrnzrAx7so1TG1yqBEL3i

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 data-reconciliation-category-frequencies-two-measure-correlation-fdf55b92 -d '<json body>'
```

Example prompt: Analyze these reconciliation records for me — I have up to 50 rows with row IDs, dates, dataset groups like 'region-A' and 'region-B', plus source_total and target_total values for each. Give me the category frequency breakdown by group and the correlation between source and target totals.

## When to prefer this

Choose this endpoint when you have data reconciliation records with source and target totals grouped by category and need both a frequency breakdown and a correlation statistic in a single low-cost call. It is ideal for ETL auditing, monthly close validation, and data migration checks where you want fast, local statistical analysis without sending data to an external model or prediction service. Prefer it over general analytics APIs when your data fits the source_total/target_total/dataset_group schema and you want both reports bundled for $0.01.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, source_total, target_total) cause row-level validation errors
- Dates not matching YYYY-MM-DD pattern will fail schema validation
- All-null source_total or target_total values may produce undefined or degenerate correlation results
- Fewer than 2 distinct groups yields a trivial frequency report
- Single-row input produces no meaningful correlation

## How this service works

Data reconciliation — Category frequencies + Two-measure correlation: Category frequencies + Two-measure correlation for supplied data reconciliation records (source_total, target_total). 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 category frequency distribution showing counts and proportions per dataset_group, and (2) a two-measure correlation report quantifying the statistical relationship between source_total and target_total values across the supplied rows. Covers up to 200 rows per call with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "source_total",
     "target_total"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "target_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_total": 10,
     "target_total": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_total": 20,
     "target_total": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_total": 15,
     "target_total": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_total": 30,
     "target_total": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-category-frequencies-two-measure-correlation-fdf55b92/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)