# Recovery Objectives Adjacent Row Changes and Missing Value Fill

> Recovery Objectives Adjacent Row Changes and Missing Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).

Computes adjacent-row delta changes and fills missing values for recovery objective records containing target and observed minutes, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-security-20-162?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/recovery-objectives-adjacent-row-changes-and-missing-value-fill-674d3ca4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_qOF_G3J0pW8JE6qNEsh2H

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 recovery-objectives-adjacent-row-changes-and-missing-value-fill-674d3ca4 -d '<json body>'
```

Example prompt: I have recovery objectives data with target_minutes and observed_minutes for several control groups across dates — can you run both an adjacent-row change analysis and a median-fill for any missing observed_minutes values on my up to 200 rows?

## When to prefer this

Choose this endpoint when you need both adjacent-row delta computation and missing-value imputation for recovery objective records in a single low-cost call. It is best suited for small-to-medium datasets (up to 200 rows) where you want offline, deterministic analysis with no external dependencies. Prefer this over generic analytics tools when your data specifically involves target_minutes and observed_minutes fields for named control groups across dates, and you want two pre-packaged reports for $0.01.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, target_minutes, observed_minutes) causes schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Unrecognized method or field enum value causes parameter rejection
- Empty rows array (minItems:1 violated) returns error
- Extreme numeric values outside allowed range rejected

## How this service works

Recovery objectives — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied recovery objectives records (target_minutes, observed_minutes). 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: (1) an adjacent-row change report showing delta values between consecutive rows for the specified field (target_minutes or observed_minutes), and (2) an explicit missing-value fill report where null entries in the specified field are replaced using the chosen method (zero, mean, or median). Results are computed locally with no external lookups and cover up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/recovery-objectives-adjacent-row-changes-and-missing-value-fill-674d3ca4/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)