菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Analyze AI config evolution in a git repo — first commits per path, monthly distribution, major PRs, maturity phases
Audit all skills in the current project for frontmatter completeness, effort level appropriateness, allowed-tools scoping, and content quality. Produces a scored report with effort-level recommendations for each skill. Use when onboarding to a new project, reviewing skill quality before shipping, or adding effort fields to an existing skill library.
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.
Launch and navigate the ccboard TUI/Web dashboard for Claude Code. Use when monitoring token usage, tracking costs, browsing sessions, or checking MCP server status across projects.
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.
Transform CHANGELOG entries into social content (LinkedIn, Twitter/X, Newsletter, Slack) in FR + EN. Use after releases or weekly to generate release notes, announcements, social media posts, or recap summaries from guide updates.
| name | voice-refine |
| description | 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. |
| allowed-tools | Read |
| context | inherit |
| agent | specialist |
| effort | low |
Transform verbose, stream-of-consciousness voice dictation into structured, token-efficient prompts for Claude Code.
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
## 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]
| 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) |
/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...
/voice-refine --direct --en
[voice input in any language → sends English prompt directly]
| Metric | Target |
|---|---|
| Token reduction | 60-70% |
| Information retention | >95% |
| Structure clarity | High |
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.
guide/ai-ecosystem.md - Voice-to-Text Tools sectionexamples/before-after.md - Full transformation examples