# Time Series Completeness — Duplicate Key Report + Date by Category Pivot

> Time Series Completeness — Duplicate Key Report + 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 2026-10-15).

Analyzes time series completeness records to produce a duplicate key report and a date-by-category pivot table for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-068?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-duplicate-key-report-date-by-category-pivot-211b8158
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FB9tLK0pP9YqdvoHf98f-

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 time-series-completeness-duplicate-key-report-date-by-category-pivot-211b8158 -d '<json body>'
```

Example prompt: Can you run a duplicate key report and date-by-category pivot on my time series completeness data? I have rows with row_id, dataset_group, date, observed_points, and expected_points — here are my 20 records, and I want to analyze the observed_points field.

## When to prefer this

Choose this endpoint when you need both duplicate key detection and date-by-category pivot analysis on time series completeness records in a single $0.01 call. Ideal when your data has fewer than 200 rows and you want offline, deterministic analysis with no external data lookups. Prefer this over general analytics APIs when your data includes observed_points and expected_points completeness metrics grouped by dataset_group and date.

## Known failure modes

- Fewer than 1 row provided — returns validation error
- More than 200 rows submitted — rejected, maxItems limit exceeded
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, date, observed_points, expected_points) — request rejected
- Invalid field enum value (not 'observed_points' or 'expected_points') — schema error
- Numeric values outside allowed range — validation error

## How this service works

Time series completeness — Duplicate key report + Date by category pivot: Duplicate key report + Date by category pivot for supplied time series completeness records (observed_points, expected_points). 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 duplicate key report identifying row_ids that appear more than once in the supplied data, and (2) a date-by-category pivot table cross-tabulating the selected metric (observed_points or expected_points) across dates and dataset_group categories. Both reports are computed locally on 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",
     "observed_points",
     "expected_points"
    ],
    "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
     },
     "expected_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observed_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_points": 1,
     "observed_points": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_points": 2,
     "observed_points": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_points": 3,
     "observed_points": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_points": 4,
     "observed_points": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "observed_points",
    "expected_points"
   ],
   "type": "string",
   "default": "observed_points"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-duplicate-key-report-date-by-category-pivot-211b8158/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)