voice-refine
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.
See Also
guide/ai-ecosystem.md- Voice-to-Text Tools sectionexamples/before-after.md- Full transformation examples
More from florianbruniaux/claude-code-ultimate-guide
rtk-optimizer
Wrap high-verbosity shell commands with RTK to reduce token consumption. Use when running git log, git diff, cargo test, pytest, or other verbose CLI output that wastes context window tokens.
140design-patterns
Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.
58landing-page-generator
Generate complete, deploy-ready landing pages from any repository. Use when creating a homepage for an open-source project, building a project website, converting a README into a marketing page, or standardizing landing pages across multiple repos.
36audit-agents-skills
Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
30release-notes-generator
Generate release notes in 3 formats (CHANGELOG.md, PR body, Slack announcement) from git commits. Automatically categorizes changes and converts technical language to user-friendly messaging. Use for releases, changelogs, version notes, what's new summaries, or ship announcements.
29talk-pipeline
Orchestrates the complete talk preparation pipeline from raw material to revision sheets, running 6 stages in sequence with human-in-the-loop checkpoints for REX or Concept mode talks. Use when starting a new talk pipeline, resuming a pipeline from a specific stage, or running the full end-to-end preparation workflow.
28