arogyareddy/https-github.com-florianbruniaux-claude-code-ultimate-guide/voice-refine

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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.

Install

npx vskill@latest install arogyareddy/https-github.com-florianbruniaux-claude-code-ultimate-guide/voice-refine

SKILL.md

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

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

FlagEffect
--confirmShow refined prompt before sending to Claude (default)
--directSend refined prompt directly without confirmation
--verboseKeep more detail, less compression
--enOutput 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

MetricTarget
Token reduction60-70%
Information retention>95%
Structure clarityHigh

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

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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Source

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