音频安装
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- 需简单配置
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- macOS · Linux · Windows
- 底层运行要求
- Python >=3.10
- 文件与系统权限
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- 只读
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- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: voice
description: > Use when this capability is needed. This skill gets the user from zero to a running voice-inpu…
category: 通用
runtime: Python
---
# voice 输出预览
## PART A: 任务判断
- 适用问题:通用任务拆解、检查和交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于通用任务拆解、检查和交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文出现了 `/dev` 这类斜杠命令;如果你的 Agent 支持命令触发,优先用命令开场,再补充目标和边界。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: voice
description: > Use when this capability is needed. This skill gets the user from zero to a running voice-inpu…
category: 通用
source: tomevault-io/skills-registry
---
# voice
## 什么时候使用
- 把通用方向的常用动作沉淀成 Agent 可调用的技能 适合处理通用任务拆解、检查、交付和复盘,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤;通常不需要额外…
- 面向通用任务拆解、检查和交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "voice" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Voice mode launcher
This skill gets the user from zero to a running voice-input loop in one go. It asks the user four quick setup questions (language, model size, hotkey, voice sensitivity), installs Python deps on first run, and launches the script as a detached background process.
Step 1 — Prerequisites
Run these checks. If either fails, tell the user what to install and stop:
python -c "import sys; assert sys.version_info >= (3,10), f'need 3.10+, have {sys.version}'; print(sys.version)"
Needs Python 3.10+. If python is not found, try python3 (macOS/Linux) or py (Windows).
If missing: "Install Python 3.10 or newer from python.org, then restart Claude."
claude --version
Needs the Claude CLI. If missing: "Install the Claude CLI first — https://claude.com/claude-code"
Step 2 — First-run setup (venv + auto-install deps)
Install into a plugin-local venv, not the user's system Python. This dodges
PEP 668 on modern Debian/Ubuntu/Fedora (where pip install --user is refused),
and keeps faster-whisper's heavy deps isolated so uninstall is just rm -rf .venv.
First check whether the venv already exists and has the deps:
# Windows
"${CLAUDE_PLUGIN_ROOT}/.venv/Scripts/python.exe" -c "import faster_whisper, sounddevice, soundfile, pynput, numpy, yaml" 2>&1
# macOS / Linux
"${CLAUDE_PLUGIN_ROOT}/.venv/bin/python" -c "import faster_whisper, sounddevice, soundfile, pynput, numpy, yaml" 2>&1
If the venv doesn't exist, create it (tell the user "Setting up voice mode (one-time, ~200 MB)..."):
# Windows
python -m venv "${CLAUDE_PLUGIN_ROOT}/.venv"
"${CLAUDE_PLUGIN_ROOT}/.venv/Scripts/python.exe" -m pip install --upgrade pip
"${CLAUDE_PLUGIN_ROOT}/.venv/Scripts/python.exe" -m pip install -r "${CLAUDE_PLUGIN_ROOT}/scripts/requirements.txt"
# macOS / Linux
python3 -m venv "${CLAUDE_PLUGIN_ROOT}/.venv"
"${CLAUDE_PLUGIN_ROOT}/.venv/bin/python" -m pip install --upgrade pip
"${CLAUDE_PLUGIN_ROOT}/.venv/bin/python" -m pip install -r "${CLAUDE_PLUGIN_ROOT}/scripts/requirements.txt"
If venv creation itself fails with "ensurepip is not available" on Debian/Ubuntu,
the user needs sudo apt install python3-venv. Surface that hint and stop.
If install itself fails, surface the error and stop.
Step 3 — Ask the user for language, model, and hotkey
Use AskUserQuestion with these FOUR questions (send all four in one call for a single round-trip — four is the AskUserQuestion tool maximum):
Question 1 — "What language will you be speaking?"
- English → code
en - French →
fr - Spanish →
es - German →
de - Italian →
it - Portuguese →
pt - Japanese →
ja - Chinese →
zh - Auto-detect → leave blank (works but less reliable on short clips)
- Other → ask for the ISO 639-1 code (e.g.
ru,nl,ko,ar,hi)
Question 2 — "Which Whisper model size?" Include a short description so the user can choose:
- tiny (~75 MB) — Fastest, roughest accuracy. English keywords or quick tests only.
- base (~150 MB) — Fast, OK English, poor for other languages.
- small (~500 MB) — Recommended. Sweet spot: good accuracy in most languages, runs on CPU.
- medium (~1.5 GB) — High accuracy but slow on CPU. Good if you have time or a GPU.
- large-v3 (~3 GB) — Near-human accuracy. Needs an NVIDIA GPU to be usable.
Question 3 — "Which hotkey should trigger recording?"
- F8 →
<f8>(Recommended — single key, no chord, rarely conflicts) - F9 →
<f9> - Ctrl + Shift + Space →
<ctrl>+<shift>+<space>(may clash with IMEs or Discord push-to-talk on some setups) - Ctrl + Shift + V →
<ctrl>+<shift>+<v> - Other → ask the user for a pynput hotkey string (each key in angle brackets, plus-separated, e.g.
<ctrl>+<alt>+<v>)
Do NOT offer Right Alt as an option on Windows. Many keyboard layouts remap Right Alt to AltGr, which Windows emits as a synthetic Ctrl+Alt combo — pynput sees two keys and the hotkey never resolves.
Question 4 — "How do you usually speak when recording?"
This picks between the default transcription pipeline and the whisper_mode
preset, which boosts mic gain and relaxes VAD/Whisper thresholds so quiet
speech doesn't get dropped as silence.
- Normal speaking voice → default (no
--whisper-modeflag) - Quietly or whispered → enable (
--whisper-mode) - Not sure → default
Defaults if the user skips: small model, auto-detect language, <f8> hotkey,
normal speaking voice.
Step 4 — Launch
Build the arg list:
- If user picked a specific language, add
--language <code> - Always add
--model <chosen_model> - If user picked a non-default hotkey, add
--hotkey "<chosen_hotkey>" - If user picked the quiet/whispered option, add
--whisper-mode
Spawn DETACHED so it survives this conversation:
Use the venv's python (created in Step 2), not the system python.
Windows: Use PowerShell Start-Process with an array argument list so paths with spaces or unicode (OneDrive, accented usernames) don't blow up the quoting:
powershell -NoProfile -Command "Start-Process -FilePath '${CLAUDE_PLUGIN_ROOT}/.venv/Scripts/python.exe' -WorkingDirectory '${CLAUDE_PLUGIN_ROOT}' -ArgumentList @('${CLAUDE_PLUGIN_ROOT}/scripts/claude_voice.py', <each-flag-as-its-own-quoted-element>)"
Example with --language fr --model small:
powershell -NoProfile -Command "Start-Process -FilePath '${CLAUDE_PLUGIN_ROOT}/.venv/Scripts/python.exe' -WorkingDirectory '${CLAUDE_PLUGIN_ROOT}' -ArgumentList @('${CLAUDE_PLUGIN_ROOT}/scripts/claude_voice.py', '--language', 'fr', '--model', 'small')"
macOS / Linux:
nohup "${CLAUDE_PLUGIN_ROOT}/.venv/bin/python" "${CLAUDE_PLUGIN_ROOT}/scripts/claude_voice.py" <flags> > /dev/null 2>&1 &
Step 5 — Confirm to the user
Tell them concisely:
- A terminal window is now open showing voice-mode logs.
- Default hotkey: F8 (or whatever they picked) — hold to talk, release to paste the transcript into the focused window.
- For the transcript to land in the Claude Desktop App, click into the chat input before releasing the hotkey. (Focus safety will block paste if the focused window title doesn't contain "Claude".)
- First launch downloads the chosen Whisper model (size depends on choice).
- To stop it: close the terminal window, or kill the python process.
Do NOT try to read the script's output — it runs independently.
Source: AfterRealm/claude-voice — distributed by TomeVault.
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核