Schema Path Flattener

Overview

You send a JSON Schema and we walk it, handing back one row per field: the path, the declared types, whether it is required, its description and format, its allowed values, and any constraint keywords it sets. Paths come out in dot notation or as JSON Pointers, your choice, and a $ref pointing inside the same document is followed with cycle protection. It is a map of the schema, not a verdict on data: to check a document against the schema, use schema.validate_json.

A deeply nested schema is hard to reason about and harder to diff. A flat row per field gives you something you can search, sort, and hand to a mapping screen.

When to use it

Scope

Non-goals

• We never check data against the schema — that is schema.validate_json
• We never follow a $ref into another document or over the network
• We never invent sample data — that is schema.generate_example_payload

Properties

Capabilities

Cost & billing

Flat rate per invocation

File handling

How we handle your data

Input schema

Output schema

API contract

curl -sS -X POST "$BASE/v1/execute" -H "Authorization: Bearer $KEY" -H 'Content-Type: application/json' -d '{"skillKey":"schema.flatten_schema_paths","input":{"schema":{"type":"object","properties":{"customer":{"type":"object","properties":{"email":{"type":"string","format":"email"}},"required":["email"]}},"required":["customer"]},"pathFormat":"dot","maxDepth":8}}'

import { Squillify } from "@squillify/sdk";

const sq = new Squillify({ apiKey: process.env.SQUILLIFY_API_KEY });

const { output } = await sq.execute("schema.flatten_schema_paths", {"schema":{"required":["id"],"type":"object","properties":{"id":{"type":"string"}}}});

Errors

Other limits

Limitations

• We never check data against the schema — that is schema.validate_json
• We never follow a $ref into another document or over the network
• We never invent sample data — that is schema.generate_example_payload
• Checking whether a document satisfies the schema
• Pulling in definitions that live in another file
• Producing a sample payload from the schema

How to call

POST /v1/execute (requires authentication)

{
  "skillKey": "schema.flatten_schema_paths",
  "input": {
    "schema": {
      "required": [
        "id"
      ],
      "type": "object",
      "properties": {
        "id": {
          "type": "string"
        }
      }
    }
  }
}

Examples

{
  "schema": {
    "required": [
      "id"
    ],
    "type": "object",
    "properties": {
      "id": {
        "type": "string"
      }
    }
  }
}

{
  "paths": [
    {
      "path": "id",
      "type": [
        "string"
      ],
      "required": true,
      "description": null,
      "format": null,
      "enum": null,
      "constraints": {}
    }
  ],
  "warnings": []
}

{
  "schema": {
    "type": "object",
    "properties": {
      "status": {
        "enum": [
          "active",
          "suspended"
        ],
        "type": "string",
        "description": "Account state"
      }
    }
  },
  "includeArrays": false,
  "pathFormat": "json_pointer"
}

{
  "paths": [
    {
      "path": "/status",
      "type": [
        "string"
      ],
      "required": false,
      "description": "Account state",
      "format": null,
      "enum": [
        "active",
        "suspended"
      ],
      "constraints": {}
    }
  ],
  "warnings": []
}

Recommended use cases

• Build a field inventory you can search, sort, or paste into a spreadsheet
• Drive a form or a field-mapping screen straight from the schema
• Compare two schemas by lining up their flattened paths

Avoid for

• Checking whether a document satisfies the schema
• Pulling in definitions that live in another file
• Producing a sample payload from the schema

Related skills

Composable with

Safety & privacy

• Runs without calling any external service.
• Your payloads are never stored permanently.
• Telemetry captures metadata only — never your payload contents.
• We never train models on your payloads.
• No human reviews your payloads.

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