CSV Schema Validator

Overview

You send CSV data together with a list of the columns you expect, and we tell you where the file disagrees: a required column that is not there, an empty cell in a column you said cannot be empty, and any value that does not parse as the type you declared. Every problem comes back with its row number, its column name, a stable code, and a readable message, so you can act on it or show it to whoever sent you the file. We check names, emptiness, and types — not ranges, patterns, or allowed-value lists. Accepts file input via file_ref — no need to extract file content first.

CSV is still how most data changes hands, and a single mistyped cell can poison an import hours after it started. Catching it at the door, with the row and column named, is cheaper than unwinding it later.

When to use it

Scope

Non-goals

• We never work out the columns for you — that is schema.infer_csv_schema
• We never clean, coerce, or reshape the data — that is data.csv_normalize
• We never check ranges, patterns, or allowed-value lists, only names, emptiness, and types

Properties

Capabilities

Cost & billing

Flat rate per invocation

File handling

File limits

Constraints this skill applies to your input and generated files. The “Limited by” column shows the static source of each cap; none of these can be raised by upgrading your plan.

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.validate_csv_schema","input":{"csv":"name,age\nAlice,30","schema":{"columns":[{"name":"name","type":"string","required":true},{"name":"age","type":"integer","required":true}]}}}'

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

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

const { output } = await sq.execute("schema.validate_csv_schema", {"csv":"name,age,email\nAlice,30,[email protected]\nBob,25,[email protected]","schema":{"columns":[{"name":"name","required":true,"type":"string"},{"name":"age","required":true,"type":"integer"},{"name":"email","required":true,"type":"string"}]}});

Errors

Other limits

Limitations

• We never work out the columns for you — that is schema.infer_csv_schema
• We never clean, coerce, or reshape the data — that is data.csv_normalize
• We never check ranges, patterns, or allowed-value lists, only names, emptiness, and types
• Discovering what the columns are in the first place (schema.infer_csv_schema)
• Cleaning or converting the data (data.csv_normalize)
• Checking JSON or XML rather than tabular data

How to call

POST /v1/execute (requires authentication)

{
  "skillKey": "schema.validate_csv_schema",
  "input": {
    "csv": "name,age,email\nAlice,30,[email protected]\nBob,25,[email protected]",
    "schema": {
      "columns": [
        {
          "name": "name",
          "required": true,
          "type": "string"
        },
        {
          "name": "age",
          "required": true,
          "type": "integer"
        },
        {
          "name": "email",
          "required": true,
          "type": "string"
        }
      ]
    }
  }
}

Examples

{
  "csv": "name,age,email\nAlice,30,[email protected]\nBob,25,[email protected]",
  "schema": {
    "columns": [
      {
        "name": "name",
        "required": true,
        "type": "string"
      },
      {
        "name": "age",
        "required": true,
        "type": "integer"
      },
      {
        "name": "email",
        "required": true,
        "type": "string"
      }
    ]
  }
}

{
  "rowCount": 2,
  "errorCount": 0,
  "valid": true,
  "errors": [],
  "warnings": [],
  "columnCount": 3
}

{
  "csv": "name,age,email\nAlice,thirty,[email protected]",
  "schema": {
    "columns": [
      {
        "name": "name",
        "required": true,
        "type": "string"
      },
      {
        "name": "age",
        "required": true,
        "type": "integer"
      },
      {
        "name": "email",
        "required": true,
        "type": "string"
      }
    ]
  }
}

{
  "rowCount": 1,
  "errorCount": 1,
  "valid": false,
  "errors": [
    {
      "code": "type_mismatch",
      "column": "age",
      "message": "Expected integer, got 'thirty'",
      "row": 1
    }
  ],
  "warnings": [],
  "columnCount": 3
}

Recommended use cases

• Check an uploaded file before you let it near your database
• Guard a scheduled import so a bad export fails loudly instead of quietly
• Tell a partner exactly which rows and columns of their file are wrong

Avoid for

• Discovering what the columns are in the first place (schema.infer_csv_schema)
• Cleaning or converting the data (data.csv_normalize)
• Checking JSON or XML rather than tabular data

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.

Tags