# Schema Conformity Analytics: Category Frequencies + Date-by-Category Pivot

> Schema Conformity Analytics: Category Frequencies + 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 2026-10-17).

Analyzes schema conformity records (valid_records, checked_records) to produce two reports: category frequency counts and a date-by-category pivot table, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-110?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-17
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/schema-conformity-analytics-category-frequencies-date-by-category-pivot-e871db07
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Yd0q_mia6x_9yzd2pD75l

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 schema-conformity-analytics-category-frequencies-date-by-category-pivot-e871db07 -d '<json body>'
```

Example prompt: I have schema conformity records for four dataset groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04 and valid_records counts. Can you give me both a category frequency breakdown and a date-by-category pivot for the valid_records field?

## When to prefer this

Choose this endpoint when you need two complementary analytical views — category frequencies and a date-by-category pivot — on schema conformity data (valid_records, checked_records) in a single low-cost call. It is ideal for data quality pipelines, validation audits, or compliance reporting where you want to understand distribution by group and trends over time without building custom aggregation logic. Prefer it over general-purpose analytics tools when your data fits the strict schema (up to 200 rows, with row_id, dataset_group, date, valid_records, checked_records) and you want a deterministic, reproducible offline computation for $0.01 USDC.

## Known failure modes

- Fewer than 1 row supplied — request rejected (minItems: 1)
- More than 200 rows supplied — request rejected (maxItems: 200)
- Missing required fields (row_id, dataset_group, date, valid_records, checked_records) — validation error
- Date field not matching YYYY-MM-DD pattern — validation error
- Invalid field enum value (not valid_records or checked_records) — validation error
- Null values in numeric fields may affect aggregation results
- Payment of 0.01 USDC not completed — request blocked by x402 payment protocol

## How this service works

Schema conformity — Category frequencies + Date by category pivot: Category frequencies + Date by category pivot for supplied schema conformity records (valid_records, checked_records). 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) category frequency counts showing how often each dataset_group appears in the supplied rows, and (2) a date-by-category pivot table showing the selected field (valid_records or checked_records) aggregated by date and dataset_group. Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-analytics-category-frequencies-date-by-category-pivot-e871db07/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)