voice
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- Python >=3.10
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 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: other
runtime: Python
---
# voice output preview
## PART A: Task fit
- Use case: > Use when this capability is needed. 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, runs entirely locally; runs on Python >=3.10. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “> Use when this capability is needed. 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, runs entirely locally; runs on Python >=3.10. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/dev`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: voice
description: > Use when this capability is needed. This skill gets the user from zero to a running voice-inpu…
category: other
source: tomevault-io/skills-registry
---
# voice
## When to use
- > Use when this capability is needed. This skill gets the user from zero to a running voice-input loop in one go. It a…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "voice" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Step 1 — Prerequisites / Step 2 — First-run setup (venv + auto-install deps) / Step 3 — Ask the user for language, model, and hotkey
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review