Unicode Text Normalizer

Overview

Normalizes Unicode text using NFC, NFD, NFKC, or NFKD forms. Optionally applies case folding, whitespace collapsing, trimming, and diacritic removal. Reports whether the text changed, code point counts, and which operations were applied. Accepts file input via file_ref — no need to extract file content first.

Visually identical text can have multiple Unicode representations — we canonicalize yours so comparison, hashing, and deduplication stay reliable.

When to use it

Scope

Non-goals

• Does not translate text
• Does not detect language
• Does not detect or repair broken encoding (mojibake)

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":"text.normalize_unicode","input":{"text":"café","form":"NFC","caseFold":true}}'

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

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

const { output } = await sq.execute("text.normalize_unicode", {"form":"NFC","text":"café"});

Errors

Other limits

Limitations

• Does not translate text
• Does not detect language
• Does not detect or repair broken encoding (mojibake)
• Translating between languages
• Detecting character encoding
• Repairing mojibake or broken encodings

How to call

POST /v1/execute (requires authentication)

{
  "skillKey": "text.normalize_unicode",
  "input": {
    "form": "NFC",
    "text": "café"
  }
}

Examples

{
  "form": "NFC",
  "text": "café"
}

{
  "inputCodePoints": 5,
  "text": "café",
  "outputCodePoints": 4,
  "changed": true,
  "offsetsPreserved": false,
  "appliedOperations": [
    "normalize:NFC"
  ]
}

{
  "text": "Résumé",
  "form": "NFD",
  "removeDiacritics": true
}

{
  "inputCodePoints": 6,
  "text": "Resume",
  "outputCodePoints": 6,
  "changed": true,
  "offsetsPreserved": true,
  "appliedOperations": [
    "normalize:NFD",
    "removeDiacritics"
  ]
}

Recommended use cases

• Normalize text before comparison or deduplication
• Prepare text for consistent hashing
• Remove diacritics for simplified search
• Canonicalize user input before database storage

Avoid for

• Translating between languages
• Detecting character encoding
• Repairing mojibake or broken encodings

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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