# Time Series Completeness — Duplicate Key Report + Adjacent Row Changes

> Time Series Completeness — Duplicate Key Report + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Runs two analytical reports on time series completeness data: a duplicate key detection report and an adjacent row change analysis, processing up to 200 rows of observed vs. expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-062?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-duplicate-key-report-adjacent-row-changes-a0b61d41
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LhqvKb6vvVj6PdcsZCx5O

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-adjacent-row-changes-a0b61d41 -d '<json body>'
```

Example prompt: Check my time series completeness records for duplicate row IDs and adjacent row changes — here are up to 50 rows with fields row_id, dataset_group, date, observed_points, and expected_points; run both reports on the observed_points field.

## When to prefer this

Choose this endpoint when you need both duplicate key detection and adjacent row change analysis on time series completeness data (observed vs. expected point counts) in a single cost-efficient call. It is best suited for data quality audits of completeness logs across dated, grouped records where you want to spot key collisions and sequential value shifts without any external data enrichment or live lookups.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, observed_points, or expected_points) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values in the selected field may produce incomplete adjacent change calculations
- Empty rows array (minItems: 1 not met) causes rejection
- Numeric values outside allowed range (-1T to +1T) fail schema validation

## How this service works

Time series completeness — Duplicate key report + Adjacent row changes: Duplicate key report + Adjacent row changes 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 reports: (1) a duplicate key report identifying rows with repeated key combinations within the dataset, and (2) an adjacent row changes report showing value differences between consecutive rows for the selected field (observed_points or expected_points). Results cover up to 200 input rows and are computed locally without 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-adjacent-row-changes-a0b61d41/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)