How do I install the Voice Refine skill?

Run npx skillmds@latest add florianbruniaux/voice-refine in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Voice Refine safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Voice Refine?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Voice Refine free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Voice Refine?

FlorianBruniaux (@florianbruniaux) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Voice Refine by florianbruniaux — Agent Skill

Name: Voice Refine
Author: FlorianBruniaux

Voice Refine

Transform verbose voice input into structured, token-efficient Claude prompts. Use when cleaning up voice memos, dictation output, or speech-to-text transcriptions that contain filler words, repetitions, and unstructured thoughts.

File contents

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

Filtering Rules

Remove: filler words ("euh", "um", "like", "basically"), repetitions, tangents, hedging ("maybe", "probably" unless relevant), politeness padding ("please", "could you").

Preserve: technical requirements, constraints, existing code context, expected output format, edge cases, business logic rules.

---name: voice-refinedescription: Transform verbose voice input into structured, token-efficient Claude prompts. Use when cleaning up voice memos, dictation output, or speech-to-text transcriptions that contain filler words, repetitions, and unstructured thoughts.---# Voice Refine SkillTransform 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 words2. EXTRACT → Identify core requirements and constraints3. STRUCTURE → Organize into standard sections4. COMPRESS → Reduce to ~30% of original while preserving intent```## Output Format```markdown## 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-refineAlors euh j'aimerais que tu m'aides à faire un truc, en fait j'ai une APIqui renvoie des données utilisateurs et je voudrais les afficher dans untableau React, mais attention il faut que ça soit paginé parce que y'abeaucoup de données, genre des milliers d'utilisateurs, et aussi faudraitpouvoir trier par nom ou par date d'inscription, ah et on utilise Tailwinddans 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 |## Filtering Rules**Remove**: filler words ("euh", "um", "like", "basically"), repetitions, tangents, hedging ("maybe", "probably" unless relevant), politeness padding ("please", "could you").**Preserve**: technical requirements, constraints, existing code context, expected output format, edge cases, business logic rules.## See Also- `guide/ai-ecosystem.md` - Voice-to-Text Tools section- `examples/before-after.md` - Full transformation examples

Frequently asked questions