Fetches natural-language from dpearson2699/swift-ios-skills and configures it for Cursor.
3
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4
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Confirm successful installation by checking the skill directory location:
.cursor/skills/natural-language
Restart Cursor to activate natural-language. Access via /natural-language in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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.
Targets Swift 6.3 / iOS 26+.
This skill covers two related frameworks: NaturalLanguage (NLTokenizer, NLTagger, NLEmbedding) for on-device text analysis, and Translation (TranslationSession, LanguageAvailability) for language translation.
Import NaturalLanguage for text analysis and Translation for language
translation. No special entitlements or capabilities are required for
NaturalLanguage. Translation requires iOS 17.4+ / macOS 14.4+.
importNaturalLanguageimportTranslation
NaturalLanguage classes (NLTokenizer, NLTagger) are not thread-safe.
Use each instance from one thread or dispatch queue at a time.
Tokenization
Segment text into words, sentences, or paragraphs with NLTokenizer.
importNaturalLanguagefunctokenizeWords(in text:String)->[String]{let tokenizer =NLTokenizer(unit:.word) tokenizer.string = text
let range = text.startIndex..<text.endIndex
return tokenizer.tokens(for: range).map {String(text[$0])}}
Token Units
Unit
Description
.word
Individual words
.sentence
Sentences
.paragraph
Paragraphs
.document
Entire document
Enumerating with Attributes
Use enumerateTokens(in:using:) to detect numeric or emoji tokens.
let tokenizer =NLTokenizer(unit:.word)tokenizer.string = text
tokenizer.enumerateTokens(in: text.startIndex..<text.endIndex){ range, attributes inif attributes.contains(.numeric){print("Number: \(text[range])")}returntrue// continue enumeration}
Language Identification
Detect the dominant language of a string with NLLanguageRecognizer.
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
