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speech-recognition

Transcribe speech to text using Apple's Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio files, handling speech and microphone authorization, choosing on-device vs server-backed SFSpeechRecognizer behavior, or adopting SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory, and async result streams on iOS 26+.

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is dpearson2699/swift-ios-skills

SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-constructed reference skill: executable code for every major flow, non-obvious API pitfalls surfaced prominently, a mistakes table plus review checklist acting as validation checkpoints, and proper deferral of advanced SpeechAnalyzer detail to a real, well-signaled reference file. Its only weakness is minor redundancy of well-known SFSpeechRecognizer boilerplate that could be trimmed.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious, high-value guidance (undocumented preset warning, one-minute task limit, volatile-result replacement, single cleanup owner) and the Common Mistakes table, but it includes some boilerplate Claude already knows, such as basic SFSpeechRecognizer construction and the delegate availability snippet. That matches anchor 4 — efficient with minor trimming possible — rather than the every-token-earns-its-place anchor 5.

4 / 5

Actionability

The skill provides complete, executable Swift for the main flows: permission request (with iOS 17 API branch), live AVAudioEngine transcription class, async file transcription, alternatives/confidence access, punctuation and contextual strings, plus a concrete SpeechAnalyzer setup checklist. It is copy-paste ready and covers the common cases, matching anchor 5.

5 / 5

Workflow Clarity

Multi-step processes are clearly sequenced: the SpeechAnalyzer checklist (choose module, check support, pick preset, install assets, convert format, consume results, explicit finalize), authorization before capture, and a single stopTranscribing() owner for cleanup across final/error paths. The Review Checklist provides an explicit validation checkpoint and the Common Mistakes table gives error-recovery guidance, matching anchor 5.

5 / 5

Progressive Disclosure

SKILL.md works as an overview with core patterns inline, and the one advanced area (SpeechAnalyzer finalization and volatile-result code) is delegated to a single, well-signaled, one-level-deep reference (references/speechanalyzer-patterns.md, verified to exist with substantive content), linked at the point of need and again from the mistakes table. A contents TOC aids navigation, matching anchor 5.

5 / 5

Total

19

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete, comprehensive, third-person, with an explicit 'Use when...' clause enumerating realistic trigger scenarios and specific Apple framework APIs. The only weakness is slightly thin natural-synonym coverage ('dictation', 'voice', 'speech recognition' as user phrases).

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'Transcribe speech to text', 'live microphone transcription with AVAudioEngine', 'recognizing recorded audio files', 'handling speech and microphone authorization', 'choosing on-device vs server-backed SFSpeechRecognizer behavior', 'adopting SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory' — covering the framework's surface comprehensively in third person. Anchor 4 would require minor coverage gaps, and none are evident for this domain.

5 / 5

Completeness

It explicitly answers both: what ('Transcribe speech to text using Apple's Speech framework') and when ('Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio files, handling speech and microphone authorization, choosing on-device vs server-backed SFSpeechRecognizer behavior, or adopting SpeechAnalyzer... on iOS 26+'). This matches the anchor 5 example structure of a clear what plus concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural-term coverage: 'transcribe', 'speech to text', 'live microphone transcription', 'recorded audio files', 'on-device', 'authorization'. However, common user synonyms like 'dictation' (present only as the API name DictationTranscriber), 'voice', and the plain phrase 'speech recognition' are missing, so it fits anchor 4 rather than the comprehensive synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

The niche is clearly pinned to Apple's Speech framework via specific API names (SFSpeechRecognizer, SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory, AVAudioEngine), giving it distinct triggers with minimal overlap risk against adjacent audio or NLP skills. It is at least as distinct as the anchor 5 example.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

dpearson2699/swift-ios-skills

Path

skills/speech-recognition/SKILL.md

Reviewed

Table of Contents

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