# Schema Conformity — Base-100 Series + Date by Category Pivot

> Schema Conformity — Base-100 Series + 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).

Generates a Base-100 indexed time series and a date-by-category pivot table from supplied schema conformity records (valid_records, checked_records) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-167?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/schema-conformity-base-100-series-date-by-category-pivot-d18d3f30
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_50yc5CFcuj0euzQ14TPcY

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-base-100-series-date-by-category-pivot-d18d3f30 -d '<json body>'
```

Example prompt: I have schema conformity data for two dataset groups over the past week — can you generate a Base-100 indexed time series and a date-by-category pivot table for my valid_records field, using the first row as the base index? The rows include row_id, dataset_group, date, valid_records, and checked_records.

## When to prefer this

Choose this endpoint when you need both a base-100 normalized time series AND a date-by-category pivot in a single low-cost call ($0.01 USDC) for schema conformity data (valid_records/checked_records) with up to 200 rows and no need for external data, live lookup, or predictive modeling. Ideal for data quality pipelines where you already have the conformity records locally and want fast, analytical summaries without spinning up your own analytics stack.

## Known failure modes

- Missing required row fields (row_id, dataset_group, date, valid_records, checked_records) cause validation errors
- Exceeding 200 rows in the array returns an error or truncation
- Invalid date format (not YYYY-MM-DD) causes parsing failure
- base_index out of range (≥ number of supplied rows) causes index error
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array (minItems:1 not satisfied) returns an error

## How this service works

Schema conformity — Base-100 series + Date by category pivot: Base-100 series + 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 analytical reports computed locally from the supplied data: (1) a Base-100 indexed time series for the selected metric field relative to the chosen base_index row, and (2) a date-by-category pivot table breaking the metric down by dataset_group across dates. No external lookups or predictions are included. Covers up to 200 input rows.

## 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-base-100-series-date-by-category-pivot-d18d3f30/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)