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natural-language

Process and translate on-device natural language text in Swift 6.3 / iOS 26+ contexts.

Updated Apr 30, 2026
Search Tags:#translation#nlp#on-device#natural-language#ios-development#text-analysis
One-click install
npx skills add https://github.com/onymchat/onym-ios --skill natural-language-onymchat
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: natural-language
Source: https://github.com/onymchat/onym-ios/tree/main/.claude/skills/natural-language
Command: npx skills add https://github.com/onymchat/onym-ios --skill natural-language-onymchat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This on-device workflow enables apps to tokenize, tag, identify language, analyze sentiment, recognize named entities, perform part-of-speech tagging, and generate word/sentence embeddings, while also translating text without relying on remote services.

Core Features & Use Cases

  • Tokenization with NLTokenizer to segment text into words, sentences, or paragraphs.
  • Language identification with NLLanguageRecognizer to detect dominant languages.
  • Named Entity Recognition and Part-of-Speech tagging with NLTagger for structured text analysis.
  • Sentiment analysis and embedding features via NLEmbedding for contextual insights.
  • Translation capabilities using the Translation framework for in-app multilingual support.

Quick Start

Import NaturalLanguage and Translation, then start analyzing text with NLTokenizer/NLTagger and translate with Translation.

Frequently Asked Questions about natural-language

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform on-device sentiment analysis and named entity recognition in Swift?▼

On-device sentiment analysis and named entity recognition use the NaturalLanguage framework with NLTagger and NLEmbedding. You can analyze text directly on iOS 17.4+ or macOS 14.4+ without relying on remote services.

Can I translate text within my iOS app without calling a remote API?▼

Yes, in-app text translation without remote services is possible using the Translation framework. You can translate text directly on-device in Swift 6.3 and iOS 26+ contexts for multilingual support.

Does NLTagger require specific iOS versions for contextual embeddings and language identification?▼

NLTagger and NLLanguageRecognizer for language identification require iOS 17.4+ or macOS 14.4+. Generating contextual embeddings with NLEmbedding may require downloading additional on-device assets.

What are the limitations of using NLTokenizer and NLTagger for text tokenization?▼

A key limitation of NLTokenizer and NLTagger is that they are not thread-safe. When segmenting text into words or sentences, you must manage concurrency carefully to avoid issues in your Swift application.

How do I segment text into sentences and identify the dominant language using NaturalLanguage?▼

To segment text and identify the dominant language, use NLTokenizer for word or sentence boundaries and NLLanguageRecognizer for detection. Both are part of the NaturalLanguage framework for on-device analysis.

What is the best way to generate word embeddings locally for text analysis?▼

The best way to generate word and sentence embeddings locally is using NLEmbedding within the NaturalLanguage framework. It provides contextual insights on-device, though additional assets may be required.

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