Voice Refine Skill

Transform verbose, stream-of-consciousness voice dictation into structured, token-efficient prompts for Claude Code.

When to Use

• Input from voice dictation (Wispr Flow, Superwhisper, macOS Dictation)

• Verbose text >150 words

• Contains filler words, repetitions, or tangents

• Natural speech patterns that need structure

Transformation Pipeline

1. DEDUPE → Remove repetitions and filler words
2. EXTRACT → Identify core requirements and constraints
3. STRUCTURE → Organize into standard sections
4. COMPRESS → Reduce to ~30% of original while preserving intent

Output Format

Contexte

[Project context, existing stack, relevant files]

Objectif

[Single sentence: what needs to be built/changed]

Contraintes

• [Constraint 1]
• [Constraint 2]
• [etc.]

Output attendu

[Expected deliverables: files, format, tests]

Flags

Flag Effect

--confirm

Show refined prompt before sending to Claude (default)

--direct

Send refined prompt directly without confirmation

--verbose

Keep more detail, less compression

--en

Output in English (default: matches input language)

Usage Examples

Basic Usage

/voice-refine

Alors euh j'aimerais que tu m'aides à faire un truc, en fait j'ai une API qui renvoie des données utilisateurs et je voudrais les afficher dans un tableau React, mais attention il faut que ça soit paginé parce que y'a beaucoup de données, genre des milliers d'utilisateurs, et aussi faudrait pouvoir trier par nom ou par date d'inscription, ah et on utilise Tailwind dans le projet donc faut que ça matche avec ça...

With Flags

/voice-refine --direct --en

[voice input in any language → sends English prompt directly]

Compression Metrics

Metric Target

Token reduction 60-70%

Information retention

95%

Structure clarity High

Integration with Voice Tools

Wispr Flow

• Dictate with Cmd+Shift+Space

• Paste into Claude Code

• Run /voice-refine

Superwhisper

• Record with hotkey

• Text appears in active window

• Run /voice-refine to structure

macOS Dictation

• Fn Fn to start

• Speak naturally

• Run /voice-refine to clean up

What Gets Removed

• Filler words: "euh", "um", "like", "you know", "basically"

• Repetitions: same concept stated multiple ways

• Tangents: off-topic thoughts

• Hedging: "maybe", "I think", "probably" (unless relevant)

• Politeness padding: "please", "could you", "I'd like"

What Gets Preserved

• Technical requirements

• Constraints and limitations

• Context about existing code

• Expected output format

• Edge cases mentioned

• Business logic rules

See Also

• guide/ai-ecosystem.md

• Voice-to-Text Tools section

• examples/before-after.md

• Full transformation examples