# Data Freshness Analytics: Two-Measure Correlation + Date-by-Category Pivot

> Data Freshness Analytics: Two-Measure Correlation + Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Runs two local analytical reports on supplied data freshness records — a Pearson/Spearman correlation between age_minutes and record_count, and a date-by-category pivot table — returning both results for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-182?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-analytics-two-measure-correlation-date-by-category-pivot-32f796da
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_rACcqD1b8W_RCxGpIT15F

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-freshness-analytics-two-measure-correlation-date-by-category-pivot-32f796da -d '<json body>'
```

Example prompt: I have a set of data freshness records with age_minutes and record_count across different dataset groups and dates — can you run a two-measure correlation between age_minutes and record_count plus a date-by-category pivot for these up to 200 rows?

## When to prefer this

Choose this endpoint when you have up to 200 data freshness records (age_minutes + record_count) and need both a statistical correlation between those two measures AND a date-by-category pivot in a single low-cost call. It is ideal for pipeline monitoring, data quality dashboards, or freshness audits where you want two complementary views — relational and dimensional — without spinning up a full analytics environment. Prefer it over general-purpose analytics services when cost-per-call matters and the dataset fits the 200-row limit.

## Known failure modes

- Fewer than 1 row supplied — minimum item count not met
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — schema validation error
- Invalid date format (must match YYYY-MM-DD pattern) — pattern validation failure
- Non-numeric or out-of-range values for age_minutes or record_count — numeric bounds exceeded
- Invalid enum value for 'field' parameter — must be 'age_minutes' or 'record_count'
- All numeric values are null — insufficient data to compute correlation
- Payment not confirmed — x402 micropayment of 0.01 USDC not completed

## How this service works

Data freshness — Two-measure correlation + Date by category pivot: Two-measure correlation + Date by category pivot for supplied data freshness records (age_minutes, record_count). 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 two-measure correlation result (e.g. Pearson/Spearman coefficient and supporting statistics) between age_minutes and record_count, and (2) a date-by-category pivot table summarizing the selected field across dataset_group and date dimensions — both computed locally from the supplied rows with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-analytics-two-measure-correlation-date-by-category-pivot-32f796da/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)