# Data Reconciliation: Base-100 Series + Two-Measure Correlation

> Data Reconciliation: Base-100 Series + 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).

Generates a base-100 index series and a two-measure correlation report for up to 200 rows of source/target reconciliation data using local analytical engines.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-165?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-base-100-series-two-measure-correlation-68809c75
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ITK1oxYW3WXPFHCfUMpC0

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-base-100-series-two-measure-correlation-68809c75 -d '<json body>'
```

Example prompt: I have a dataset of daily source and target revenue figures across two groups — can you rebase the source_total series to 100 starting from row 0 and also run a two-measure correlation between source_total and target_total? Here are up to 50 rows with row_id, date (YYYY-MM-DD), dataset_group, source_total, and target_total.

## When to prefer this

Choose this endpoint when you have already collected source and target numeric totals and need two offline analytical outputs — a base-100 time series and a correlation coefficient — without any external data fetching, prediction, or professional assessment. It is ideal for reconciliation workflows where you want to normalize a series to a common baseline and simultaneously quantify how closely the two measures track each other, all in a single low-cost call for up to 200 rows.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — request rejected
- base_index value exceeds the number of supplied rows — index out of bounds error
- Null values in source_total or target_total may affect correlation calculation
- Dates not matching YYYY-MM-DD pattern — schema validation failure
- row_id or dataset_group fields exceeding 80-character limit — validation error
- Values outside the ±1,000,000,000,000 range — rejected by schema bounds

## How this service works

Data reconciliation — Base-100 series + Two-measure correlation: Base-100 series + 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 analytical reports: (1) a base-100 index series rebasing the chosen field (source_total or target_total) relative to a specified row, and (2) a two-measure correlation report quantifying the statistical relationship between source_total and target_total across the supplied rows. No external data is fetched; all computation is performed on the submitted data using local analytical engines.

## 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
  },
  "field": {
   "enum": [
    "source_total",
    "target_total"
   ],
   "type": "string",
   "default": "source_total"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-base-100-series-two-measure-correlation-68809c75/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)