Natural Language
Apple's NaturalLanguage and Translation frameworks give you on-device text analysis and language translation without sending data to the cloud. You get tokenization, part-of-speech tagging, named entity recognition, sentiment scoring, and text embeddings for semantic similarity. The Translation framework adds both a system UI overlay and programmatic translation with batch support. NLTagger and NLTokenizer aren't thread-safe, so keep instances on one queue. Sentiment scores range from negative one to positive one, and embeddings work at both word and sentence level. Useful when you need language features in iOS or macOS apps without the latency or privacy concerns of API calls.
- skills.sh installs
- 3.8K
- GitHub stars
- 1.3K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add dpearson2699/swift-ios-skills --skill natural-language --agent claude-codeInstalls into .claude/skills of the current project.
Files
NaturalLanguage + Translation
Analyze natural language text for tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, language identification, and word/sentence embeddings. Translate text between languages with the Translation framework.
This skill covers two related frameworks: NaturalLanguage (
NLTokenizer,NLTagger,NLEmbedding) for on-device text analysis, and Translation (TranslationSession,LanguageAvailability) for language translation.
Scope boundary: Use this skill after you already have text. It owns
tokenization, language identification, POS/NER tagging, sentiment, embeddings,
custom NLModel classifiers/taggers, and in-app translation. Hand off OCR to
vision-framework, speech-to-text to speech-recognition, UI strings and
locale formatting to ios-localization, and generative summarization or Apple
Intelligence workflows to apple-on-device-ai.
Contents
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- Repository
- github.com/dpearson2699/swift-ios-skills
- Categories
- Release Management, Automation & Workflows, Mobile Development
- First seen
- Apr 16, 2026
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