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Foundation Text Utilities Reference

Foundation Text Utilities Reference

Use when using Foundation or NaturalLanguage text utilities — NSRegularExpression, NSDataDetector, NLTagger, NLTokenizer, or NSString bridging.

Reference Skills

Use when using Foundation or NaturalLanguage text utilities — NSRegularExpression, NSDataDetector, NLTagger, NLTokenizer, or NSString bridging.

Family: Text Model And Foundation Utilities

Use this skill when you need the exact Foundation or NaturalLanguage tool for a text-processing problem.

When to Use

Section titled “When to Use”
  • You need NSRegularExpression, NSDataDetector, or NaturalLanguage APIs.
  • You are measuring text or bridging String and NSString.
  • The question is about utility APIs, not parser choice alone.

Quick Decision

Section titled “Quick Decision”
  • Need parser choice guidance -> /skill apple-text-parsing
  • Need the exact utility API or compatibility details -> stay here
  • Need attributed-text model guidance instead of utilities -> /skill apple-text-attributed-string

Core Guidance

Section titled “Core Guidance”

NSRegularExpression

Section titled “NSRegularExpression”

ICU-compatible regex engine. Reference type.

let pattern = "\\b[A-Z][a-z]+\\b"
let regex = try NSRegularExpression(pattern: pattern, options: [.caseInsensitive])


// Find all matches
let text = "Hello World from Swift"
let fullRange = NSRange(text.startIndex..., in: text)
let matches = regex.matches(in: text, range: fullRange)


for match in matches {
    if let range = Range(match.range, in: text) {
        print(text[range])
    }
}


// First match only
let firstMatch = regex.firstMatch(in: text, range: fullRange)


// Number of matches
let count = regex.numberOfMatches(in: text, range: fullRange)


// Replace
let replaced = regex.stringByReplacingMatches(
    in: text, range: fullRange,
    withTemplate: "[$0]"
)


// Enumerate matches
regex.enumerateMatches(in: text, range: fullRange) { result, flags, stop in
    guard let result else { return }
    // Process match
}

Options

Section titled “Options”
NSRegularExpression.Options:
    .caseInsensitive      // i
    .allowCommentsAndWhitespace  // x
    .ignoreMetacharacters  // literal match
    .dotMatchesLineSeparators    // s
    .anchorsMatchLines     // m
    .useUnixLineSeparators
    .useUnicodeWordBoundaries

Capture Groups

Section titled “Capture Groups”
let regex = try NSRegularExpression(pattern: "(\\w+)@(\\w+\\.\\w+)")
let text = "user@example.com"
if let match = regex.firstMatch(in: text, range: NSRange(text.startIndex..., in: text)) {
    // match.range(at: 0) — full match
    // match.range(at: 1) — first group ("user")
    // match.range(at: 2) — second group ("example.com")
    let user = String(text[Range(match.range(at: 1), in: text)!])
    let domain = String(text[Range(match.range(at: 2), in: text)!])
}

Modern Alternative: Swift Regex (iOS 16+)

Section titled “Modern Alternative: Swift Regex (iOS 16+)”
let regex = /(?<user>\w+)@(?<domain>\w+\.\w+)/
if let match = text.firstMatch(of: regex) {
    let user = match.user
    let domain = match.domain
}


// With RegexBuilder
import RegexBuilder
let pattern = Regex {
    Capture { OneOrMore(.word) }
    "@"
    Capture { OneOrMore(.word); "."; OneOrMore(.word) }
}

When to use NSRegularExpression vs Swift Regex:

  • NSRegularExpression: Dynamic patterns (user input), pre-iOS 16, NSRange-based APIs
  • Swift Regex: Static patterns, type-safe captures, iOS 16+

NSDataDetector

Section titled “NSDataDetector”

Detects semantic data in natural language text. Subclass of NSRegularExpression.

let types: NSTextCheckingResult.CheckingType = [.link, .phoneNumber, .address, .date]
let detector = try NSDataDetector(types: types.rawValue)


let text = "Call 555-1234 on March 15, 2025 or visit https://apple.com"
let matches = detector.matches(in: text, range: NSRange(text.startIndex..., in: text))


for match in matches {
    switch match.resultType {
    case .link:
        print("URL: \(match.url!)")
    case .phoneNumber:
        print("Phone: \(match.phoneNumber!)")
    case .address:
        print("Address: \(match.addressComponents!)")
    case .date:
        print("Date: \(match.date!)")
    case .transitInformation:
        print("Flight: \(match.components!)")
    default: break
    }
}

Supported Types

Section titled “Supported Types”
TypePropertiesExample
.linkurl”https://apple.com”
.phoneNumberphoneNumber”555-1234”
.addressaddressComponents”1 Apple Park Way, Cupertino”
.datedate, duration, timeZone”March 15, 2025”
.transitInformationcomponents (airline, flight)“UA 123”

Modern Alternative: DataDetection (iOS 18+)

Section titled “Modern Alternative: DataDetection (iOS 18+)”
import DataDetection
// New API with structured results and better accuracy

NaturalLanguage Framework (iOS 12+)

Section titled “NaturalLanguage Framework (iOS 12+)”

Replaces deprecated NSLinguisticTagger.

NLTagger

Section titled “NLTagger”

Tag text with linguistic information:

import NaturalLanguage


let tagger = NLTagger(tagSchemes: [.lexicalClass, .nameType, .lemma])
tagger.string = "Apple released new iPhones in Cupertino"


// Enumerate tags
tagger.enumerateTags(
    in: tagger.string!.startIndex..<tagger.string!.endIndex,
    unit: .word,
    scheme: .lexicalClass
) { tag, range in
    if let tag {
        print("\(tagger.string![range]): \(tag.rawValue)")
        // "Apple": Noun, "released": Verb, etc.
    }
    return true
}

Tag Schemes

Section titled “Tag Schemes”
SchemeTagsPurpose
.tokenType.word, .punctuation, .whitespaceToken classification
.lexicalClass.noun, .verb, .adjective, .adverb, etc.Part of speech
.nameType.personalName, .placeName, .organizationNameNamed entity recognition
.lemma(base form string)Word lemmatization
.language(BCP 47 code)Per-word language
.script(ISO 15924 code)Writing script

NLTokenizer

Section titled “NLTokenizer”

Segment text into tokens:

let tokenizer = NLTokenizer(unit: .word)  // .word, .sentence, .paragraph, .document
tokenizer.string = "Hello, world! How are you?"


tokenizer.enumerateTokens(in: tokenizer.string!.startIndex..<tokenizer.string!.endIndex) { range, attrs in
    print(tokenizer.string![range])
    return true
}
// Output: "Hello", "world", "How", "are", "you"

NLLanguageRecognizer

Section titled “NLLanguageRecognizer”

Identify language of text:

let recognizer = NLLanguageRecognizer()
recognizer.processString("Bonjour le monde")
let language = recognizer.dominantLanguage  // .french


// With probabilities
let hypotheses = recognizer.languageHypotheses(withMaximum: 3)
// [.french: 0.95, .italian: 0.03, .spanish: 0.02]


// Constrain to specific languages
recognizer.languageConstraints = [.english, .french, .german]


// Language hints (prior probabilities)
recognizer.languageHints = [.french: 0.8, .english: 0.2]

NLEmbedding

Section titled “NLEmbedding”

Word and sentence embeddings for semantic similarity:

// Built-in word embeddings
if let embedding = NLEmbedding.wordEmbedding(for: .english) {
    let distance = embedding.distance(between: "king", and: "queen")


    // Find nearest neighbors
    embedding.enumerateNeighbors(for: "swift", maximumCount: 5) { neighbor, distance in
        print("\(neighbor): \(distance)")
        return true
    }
}


// Sentence embedding (iOS 14+)
if let sentenceEmbedding = NLEmbedding.sentenceEmbedding(for: .english) {
    let distance = sentenceEmbedding.distance(
        between: "The cat sat on the mat",
        and: "A feline rested on the rug"
    )
}

Custom NLModel (via Create ML)

Section titled “Custom NLModel (via Create ML)”
// Load trained model
let model = try NLModel(mlModel: MyTextClassifier().model)


// Classify text
let label = model.predictedLabel(for: "This is great!")
// e.g., "positive"


// With confidence
let hypotheses = model.predictedLabelHypotheses(for: "This is great!", maximumCount: 3)

NSStringDrawingContext

Section titled “NSStringDrawingContext”

Controls text drawing behavior, especially scaling:

let context = NSStringDrawingContext()
context.minimumScaleFactor = 0.5  // Allow shrinking to 50%


let boundingRect = CGRect(x: 0, y: 0, width: 200, height: 50)
attributedString.draw(with: boundingRect,
                      options: [.usesLineFragmentOrigin],
                      context: context)


// Check what scale was actually used
print("Scale used: \(context.actualScaleFactor)")
// 1.0 = no shrinking needed, < 1.0 = text was shrunk

Bounding Rect Calculation

Section titled “Bounding Rect Calculation”
// Calculate size needed for attributed string
let size = attributedString.boundingRect(
    with: CGSize(width: maxWidth, height: .greatestFiniteMagnitude),
    options: [.usesLineFragmentOrigin, .usesFontLeading],
    context: nil
).size


// Round up for pixel alignment
let ceilSize = CGSize(width: ceil(size.width), height: ceil(size.height))

Options:

  • .usesLineFragmentOrigin — Multi-line text (ALWAYS include for multi-line)
  • .usesFontLeading — Include font leading in height
  • .truncatesLastVisibleLine — Truncate if exceeds bounds

String / NSString Bridging

Section titled “String / NSString Bridging”

Key Differences

Section titled “Key Differences”
AspectString (Swift)NSString (ObjC)
EncodingUTF-8 internalUTF-16 internal
IndexingString.Index (Character)Int (UTF-16 code unit)
Count.count (Characters).length (UTF-16 units)
Empty check.isEmpty.length == 0
TypeValue typeReference type

Bridging Cost

Section titled “Bridging Cost”
let swiftStr: String = "Hello"
let nsStr = swiftStr as NSString     // Bridge (may defer copy)
let backStr = nsStr as String         // Bridge back


// NSRange ↔ Range conversion
let nsRange = NSRange(swiftStr.startIndex..., in: swiftStr)
let swiftRange = Range(nsRange, in: swiftStr)

Performance note: Bridging is NOT zero-cost. UTF-8 ↔ UTF-16 conversion may occur. For tight loops with Foundation APIs, consider working with NSString directly.

Common Pattern: NSRange from String

Section titled “Common Pattern: NSRange from String”
let text = "Hello 👋🏽 World"


// ✅ CORRECT: Using String for conversion
let nsRange = NSRange(text.range(of: "World")!, in: text)


// ✅ CORRECT: Full range
let fullRange = NSRange(text.startIndex..., in: text)


// ❌ WRONG: Assuming character count = NSString length
let badRange = NSRange(location: 0, length: text.count)  // WRONG for emoji/CJK

Why counts differ: "👋🏽".count = 1 (one Character), ("👋🏽" as NSString).length = 4 (four UTF-16 code units).

Quick Reference

Section titled “Quick Reference”
NeedAPIMin OS
Pattern matching (dynamic)NSRegularExpressionAll
Pattern matching (static)Swift RegexiOS 16
Detect links/phones/datesNSDataDetectorAll
Detect data (modern)DataDetectioniOS 18
Part of speech taggingNLTagger (.lexicalClass)iOS 12
Named entity recognitionNLTagger (.nameType)iOS 12
Language detectionNLLanguageRecognizeriOS 12
Text segmentationNLTokenizeriOS 12
Word similarityNLEmbedding.wordEmbeddingiOS 13
Sentence similarityNLEmbedding.sentenceEmbeddingiOS 14
Custom classifierNLModel + Create MLiOS 12
Text measurementNSAttributedString.boundingRectAll
Draw text with scalingNSStringDrawingContextAll

Common Pitfalls

Section titled “Common Pitfalls”
  1. Assuming String.count == NSString.length — They use different counting units (Characters vs UTF-16). Always convert ranges explicitly.
  2. Missing .usesLineFragmentOrigin — Without this option, boundingRect calculates for single-line text.
  3. NSRegularExpression with user input — Always try the constructor — invalid patterns throw.
  4. NLTagger requires enough text — Very short strings produce unreliable linguistic analysis.
  5. Bridging in hot loops — String ↔ NSString conversion has overhead. Keep one type in tight loops.

Documentation Scope

Section titled “Documentation Scope”

This page documents the apple-text-foundation-ref reference skill. Use it when the subsystem is already known and you need mechanics, behavior, or API detail.

Section titled “Related”
  • apple-text-parsing: Use when deciding between Swift Regex and NSRegularExpression, bridging regex results to NSRange, or choosing a parsing strategy.
  • apple-text-attributed-string: Use when choosing between AttributedString and NSAttributedString, defining custom attributes, or converting between them.
  • apple-text-markdown: Use when working with Markdown in SwiftUI Text or AttributedString — what renders, PresentationIntent, or rendering gaps.
Full SKILL.md source
SKILL.md
---
name: apple-text-foundation-ref
description: Use when using Foundation or NaturalLanguage text utilities — NSRegularExpression, NSDataDetector, NLTagger, NLTokenizer, or NSString bridging
license: MIT
---


# Foundation Text Utilities Reference


Use this skill when you need the exact Foundation or NaturalLanguage tool for a text-processing problem.


## When to Use


- You need `NSRegularExpression`, `NSDataDetector`, or NaturalLanguage APIs.
- You are measuring text or bridging `String` and `NSString`.
- The question is about utility APIs, not parser choice alone.


## Quick Decision


- Need parser choice guidance -> `/skill apple-text-parsing`
- Need the exact utility API or compatibility details -> stay here
- Need attributed-text model guidance instead of utilities -> `/skill apple-text-attributed-string`


## Core Guidance


## NSRegularExpression


ICU-compatible regex engine. Reference type.


```swift
let pattern = "\\b[A-Z][a-z]+\\b"
let regex = try NSRegularExpression(pattern: pattern, options: [.caseInsensitive])


// Find all matches
let text = "Hello World from Swift"
let fullRange = NSRange(text.startIndex..., in: text)
let matches = regex.matches(in: text, range: fullRange)


for match in matches {
    if let range = Range(match.range, in: text) {
        print(text[range])
    }
}


// First match only
let firstMatch = regex.firstMatch(in: text, range: fullRange)


// Number of matches
let count = regex.numberOfMatches(in: text, range: fullRange)


// Replace
let replaced = regex.stringByReplacingMatches(
    in: text, range: fullRange,
    withTemplate: "[$0]"
)


// Enumerate matches
regex.enumerateMatches(in: text, range: fullRange) { result, flags, stop in
    guard let result else { return }
    // Process match
}
```


### Options


```swift
NSRegularExpression.Options:
    .caseInsensitive      // i
    .allowCommentsAndWhitespace  // x
    .ignoreMetacharacters  // literal match
    .dotMatchesLineSeparators    // s
    .anchorsMatchLines     // m
    .useUnixLineSeparators
    .useUnicodeWordBoundaries
```


### Capture Groups


```swift
let regex = try NSRegularExpression(pattern: "(\\w+)@(\\w+\\.\\w+)")
let text = "user@example.com"
if let match = regex.firstMatch(in: text, range: NSRange(text.startIndex..., in: text)) {
    // match.range(at: 0) — full match
    // match.range(at: 1) — first group ("user")
    // match.range(at: 2) — second group ("example.com")
    let user = String(text[Range(match.range(at: 1), in: text)!])
    let domain = String(text[Range(match.range(at: 2), in: text)!])
}
```


### Modern Alternative: Swift Regex (iOS 16+)


```swift
let regex = /(?<user>\w+)@(?<domain>\w+\.\w+)/
if let match = text.firstMatch(of: regex) {
    let user = match.user
    let domain = match.domain
}


// With RegexBuilder
import RegexBuilder
let pattern = Regex {
    Capture { OneOrMore(.word) }
    "@"
    Capture { OneOrMore(.word); "."; OneOrMore(.word) }
}
```


**When to use NSRegularExpression vs Swift Regex:**
- NSRegularExpression: Dynamic patterns (user input), pre-iOS 16, NSRange-based APIs
- Swift Regex: Static patterns, type-safe captures, iOS 16+


## NSDataDetector


Detects semantic data in natural language text. Subclass of NSRegularExpression.


```swift
let types: NSTextCheckingResult.CheckingType = [.link, .phoneNumber, .address, .date]
let detector = try NSDataDetector(types: types.rawValue)


let text = "Call 555-1234 on March 15, 2025 or visit https://apple.com"
let matches = detector.matches(in: text, range: NSRange(text.startIndex..., in: text))


for match in matches {
    switch match.resultType {
    case .link:
        print("URL: \(match.url!)")
    case .phoneNumber:
        print("Phone: \(match.phoneNumber!)")
    case .address:
        print("Address: \(match.addressComponents!)")
    case .date:
        print("Date: \(match.date!)")
    case .transitInformation:
        print("Flight: \(match.components!)")
    default: break
    }
}
```


### Supported Types


| Type | Properties | Example |
|------|-----------|---------|
| `.link` | `url` | "https://apple.com" |
| `.phoneNumber` | `phoneNumber` | "555-1234" |
| `.address` | `addressComponents` | "1 Apple Park Way, Cupertino" |
| `.date` | `date`, `duration`, `timeZone` | "March 15, 2025" |
| `.transitInformation` | `components` (airline, flight) | "UA 123" |


### Modern Alternative: DataDetection (iOS 18+)


```swift
import DataDetection
// New API with structured results and better accuracy
```


## NaturalLanguage Framework (iOS 12+)


Replaces deprecated `NSLinguisticTagger`.


### NLTagger


Tag text with linguistic information:


```swift
import NaturalLanguage


let tagger = NLTagger(tagSchemes: [.lexicalClass, .nameType, .lemma])
tagger.string = "Apple released new iPhones in Cupertino"


// Enumerate tags
tagger.enumerateTags(
    in: tagger.string!.startIndex..<tagger.string!.endIndex,
    unit: .word,
    scheme: .lexicalClass
) { tag, range in
    if let tag {
        print("\(tagger.string![range]): \(tag.rawValue)")
        // "Apple": Noun, "released": Verb, etc.
    }
    return true
}
```


### Tag Schemes


| Scheme | Tags | Purpose |
|--------|------|---------|
| `.tokenType` | `.word`, `.punctuation`, `.whitespace` | Token classification |
| `.lexicalClass` | `.noun`, `.verb`, `.adjective`, `.adverb`, etc. | Part of speech |
| `.nameType` | `.personalName`, `.placeName`, `.organizationName` | Named entity recognition |
| `.lemma` | (base form string) | Word lemmatization |
| `.language` | (BCP 47 code) | Per-word language |
| `.script` | (ISO 15924 code) | Writing script |


### NLTokenizer


Segment text into tokens:


```swift
let tokenizer = NLTokenizer(unit: .word)  // .word, .sentence, .paragraph, .document
tokenizer.string = "Hello, world! How are you?"


tokenizer.enumerateTokens(in: tokenizer.string!.startIndex..<tokenizer.string!.endIndex) { range, attrs in
    print(tokenizer.string![range])
    return true
}
// Output: "Hello", "world", "How", "are", "you"
```


### NLLanguageRecognizer


Identify language of text:


```swift
let recognizer = NLLanguageRecognizer()
recognizer.processString("Bonjour le monde")
let language = recognizer.dominantLanguage  // .french


// With probabilities
let hypotheses = recognizer.languageHypotheses(withMaximum: 3)
// [.french: 0.95, .italian: 0.03, .spanish: 0.02]


// Constrain to specific languages
recognizer.languageConstraints = [.english, .french, .german]


// Language hints (prior probabilities)
recognizer.languageHints = [.french: 0.8, .english: 0.2]
```


### NLEmbedding


Word and sentence embeddings for semantic similarity:


```swift
// Built-in word embeddings
if let embedding = NLEmbedding.wordEmbedding(for: .english) {
    let distance = embedding.distance(between: "king", and: "queen")


    // Find nearest neighbors
    embedding.enumerateNeighbors(for: "swift", maximumCount: 5) { neighbor, distance in
        print("\(neighbor): \(distance)")
        return true
    }
}


// Sentence embedding (iOS 14+)
if let sentenceEmbedding = NLEmbedding.sentenceEmbedding(for: .english) {
    let distance = sentenceEmbedding.distance(
        between: "The cat sat on the mat",
        and: "A feline rested on the rug"
    )
}
```


### Custom NLModel (via Create ML)


```swift
// Load trained model
let model = try NLModel(mlModel: MyTextClassifier().model)


// Classify text
let label = model.predictedLabel(for: "This is great!")
// e.g., "positive"


// With confidence
let hypotheses = model.predictedLabelHypotheses(for: "This is great!", maximumCount: 3)
```


## NSStringDrawingContext


Controls text drawing behavior, especially scaling:


```swift
let context = NSStringDrawingContext()
context.minimumScaleFactor = 0.5  // Allow shrinking to 50%


let boundingRect = CGRect(x: 0, y: 0, width: 200, height: 50)
attributedString.draw(with: boundingRect,
                      options: [.usesLineFragmentOrigin],
                      context: context)


// Check what scale was actually used
print("Scale used: \(context.actualScaleFactor)")
// 1.0 = no shrinking needed, < 1.0 = text was shrunk
```


### Bounding Rect Calculation


```swift
// Calculate size needed for attributed string
let size = attributedString.boundingRect(
    with: CGSize(width: maxWidth, height: .greatestFiniteMagnitude),
    options: [.usesLineFragmentOrigin, .usesFontLeading],
    context: nil
).size


// Round up for pixel alignment
let ceilSize = CGSize(width: ceil(size.width), height: ceil(size.height))
```


**Options:**
- `.usesLineFragmentOrigin` — Multi-line text (ALWAYS include for multi-line)
- `.usesFontLeading` — Include font leading in height
- `.truncatesLastVisibleLine` — Truncate if exceeds bounds


## String / NSString Bridging


### Key Differences


| Aspect | String (Swift) | NSString (ObjC) |
|--------|---------------|-----------------|
| **Encoding** | UTF-8 internal | UTF-16 internal |
| **Indexing** | `String.Index` (Character) | `Int` (UTF-16 code unit) |
| **Count** | `.count` (Characters) | `.length` (UTF-16 units) |
| **Empty check** | `.isEmpty` | `.length == 0` |
| **Type** | Value type | Reference type |


### Bridging Cost


```swift
let swiftStr: String = "Hello"
let nsStr = swiftStr as NSString     // Bridge (may defer copy)
let backStr = nsStr as String         // Bridge back


// NSRange ↔ Range conversion
let nsRange = NSRange(swiftStr.startIndex..., in: swiftStr)
let swiftRange = Range(nsRange, in: swiftStr)
```


**Performance note:** Bridging is NOT zero-cost. UTF-8 ↔ UTF-16 conversion may occur. For tight loops with Foundation APIs, consider working with NSString directly.


### Common Pattern: NSRange from String


```swift
let text = "Hello 👋🏽 World"


// ✅ CORRECT: Using String for conversion
let nsRange = NSRange(text.range(of: "World")!, in: text)


// ✅ CORRECT: Full range
let fullRange = NSRange(text.startIndex..., in: text)


// ❌ WRONG: Assuming character count = NSString length
let badRange = NSRange(location: 0, length: text.count)  // WRONG for emoji/CJK
```


**Why counts differ:** `"👋🏽".count` = 1 (one Character), `("👋🏽" as NSString).length` = 4 (four UTF-16 code units).


## Quick Reference


| Need | API | Min OS |
|------|-----|--------|
| Pattern matching (dynamic) | NSRegularExpression | All |
| Pattern matching (static) | Swift Regex | iOS 16 |
| Detect links/phones/dates | NSDataDetector | All |
| Detect data (modern) | DataDetection | iOS 18 |
| Part of speech tagging | NLTagger (.lexicalClass) | iOS 12 |
| Named entity recognition | NLTagger (.nameType) | iOS 12 |
| Language detection | NLLanguageRecognizer | iOS 12 |
| Text segmentation | NLTokenizer | iOS 12 |
| Word similarity | NLEmbedding.wordEmbedding | iOS 13 |
| Sentence similarity | NLEmbedding.sentenceEmbedding | iOS 14 |
| Custom classifier | NLModel + Create ML | iOS 12 |
| Text measurement | NSAttributedString.boundingRect | All |
| Draw text with scaling | NSStringDrawingContext | All |


## Common Pitfalls


1. **Assuming String.count == NSString.length** — They use different counting units (Characters vs UTF-16). Always convert ranges explicitly.
2. **Missing `.usesLineFragmentOrigin`** — Without this option, `boundingRect` calculates for single-line text.
3. **NSRegularExpression with user input** — Always `try` the constructor — invalid patterns throw.
4. **NLTagger requires enough text** — Very short strings produce unreliable linguistic analysis.
5. **Bridging in hot loops** — String ↔ NSString conversion has overhead. Keep one type in tight loops.


## Related Skills


- Use `/skill apple-text-parsing` for Swift Regex vs `NSRegularExpression` choice.
- Use `/skill apple-text-markdown` when parsing feeds Markdown-rendering workflows.
- Use `/skill apple-text-attributed-string` when utility output becomes attributed content.
Apple Text docs for TextKit, Apple text views, and Writing Tools. Source on GitHub.
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