agentleFS
Sign inStart free
crisandrewscrisandrews/ClawCode/skills/voice/SKILL.md

voice

Skill64 starsChanged 2 months agoMITView on GitHub

Voice — text-to-speech and transcription. Triggers on /agent:voice, /agent:voice status, /agent:voice setup, /agent:voice test, "configurar voz", "prueba voz", "voice setup", "speak this", "read this aloud", "transcribe audio".

  • Reads credentials
  • Installs packages

33 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use claude-buddy, face-enroll and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • autonomous-aiSkill

    claude-buddy

    Coordinate with the Claude Buddy companion (Claude Desktop + Claude Code on the user's Mac) — voice-approve tool permission prompts and stay aware of Claude activity.

    Also in 3 projects that use this

  • autonomous-aiSkill

    face-enroll

    Manage the device's face recognition roster — enroll new faces (3 paths: user-supplied photo, agent-captured snapshot on user request, or HAL's familiar-stranger prompt) and maintain the enrolled set (status / remove / reset). All enrolled persons are friends; strangers stay unnamed until promoted via one of the enroll flows.

    Also in 3 projects that use this

  • +10 more from autonomous-ai

The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 1 is distinctive, and 9 are very specific to this project.

  • 0

    Common

    Most of the 25 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 9

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Mention WhatsApp precedence: if the WhatsApp plugin is configured:

    line 35
  2. Very specific:

    If voice_status reports whatsapp.audioEnabled: true — *"Your WhatsApp plugin already transcribes voice notes locally.

    line 36
  3. Very specific:

    If false — *"Your WhatsApp plugin doesn't transcribe by default.

    line 37
  4. Very specific:

    Call voice_status to confirm voice is enabled AND a backend is available.

    line 41
  5. Very specific:

    If the user is on a messaging channel that supports media, use the channel's own reply/send tool with the path (e.g.

    line 44
  6. Very specific:

    Never put API keys in agent-config.json.

    line 48
  7. Very specific:

    Add them to ~/.zshrc / ~/.bashrc / ~/.config/fish/config.fish for persistence.

    line 55
  8. Very specific:

    Don't write API keys to agent-config.json — they're env-only.

    line 76
  9. Very specific:

    Don't auto-run brew install — always let the user do it.

    line 77
  10. Very specific:

    Don't transcribe WhatsApp voice notes with voice_transcribe if the plugin has audio-on.

    line 78

“Common” is measured: kept by at least 6 in 10 of the 25 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Voice — TTS and transcription

Set up and test the agent's voice backends. Voice is OPTIONAL — off by default. See docs/voice.md for the full reference including channel-plugin precedence.

Dispatch

User saysAction
/agent:voice (no arg) or /agent:voice statusCall voice_status and print the card
/agent:voice setupGuided setup (see flow below)
/agent:voice testCall voice_speak({ text: "Hola, soy <name>. Esta es una prueba.", ... }) using the user's language, report the path

The agent invokes voice_speak / voice_transcribe directly when it needs to produce or consume audio during regular conversation. This skill is only for setup and diagnostics.

Setup flow

  1. Call voice_status({ format: "json" }) to see current state.
  2. If no TTS backend is available, suggest in order of preference:
    • sag (recommended): "Run brew install steipete/tap/sag. It's a small wrapper around ElevenLabs with good voice-prompting conventions."
    • OpenAI TTS: "Set export OPENAI_API_KEY=... in your shell rc (~/.zshrc or ~/.bashrc). Then restart the agent."
    • macOS say: "Built in on macOS. Sounds robotic but zero setup. No action needed — will be used as fallback."
  3. If sag is installed but ELEVENLABS_API_KEY is missing, instruct: "Get a key from https://elevenlabs.io. Add export ELEVENLABS_API_KEY=sk_... to your shell rc. Restart the agent."
  4. If the user also wants STT, same logic: recommend whisper-cli (brew install whisper-cpp, offline, free) or OpenAI Whisper API (same OPENAI_API_KEY).
  5. Enable voice in config:
    • Tell the user: "I'll set voice.enabled: true in your config. Run agent_config(action='set', key='voice.enabled', value='true') or edit agent-config.json directly."
    • (You MAY call agent_config for them after they confirm.)
  6. If the sag skill is in an OpenClaw workspace (~/.openclaw/workspace*/skills/sag/), offer: "I see you have the sag skill in an OpenClaw workspace. Want me to install it into this agent? Run /agent:skill install <that path>."
  7. Mention WhatsApp precedence if the WhatsApp plugin is configured:
    • If voice_status reports whatsapp.audioEnabled: true — "Your WhatsApp plugin already transcribes voice notes locally. For inbound WhatsApp audio you don't need voice_transcribe. Setting this up is for WebChat uploads, iMessage audio, outbound voice notes, etc."
    • If false — "Your WhatsApp plugin doesn't transcribe by default. Either turn that on with /whatsapp:configure audio (local Whisper, free), or use our voice_transcribe per message."

Test flow

  1. Call voice_status to confirm voice is enabled AND a backend is available. If not, redirect to setup.
  2. Call voice_speak({ text: "<greeting in user's language>" }).
  3. Print the result: "Generated at /tmp/... using backend X. Play it or attach it in a messaging channel."
  4. If the user is on a messaging channel that supports media, use the channel's own reply/send tool with the path (e.g. MEDIA:/tmp/... or a dedicated send_media tool).

Secrets handling

Never put API keys in agent-config.json. They are SECRETS and the config file may end up in a git repo. Use environment variables:

export ELEVENLABS_API_KEY=sk_...
export OPENAI_API_KEY=sk-...

Add them to ~/.zshrc / ~/.bashrc / ~/.config/fish/config.fish for persistence.

Non-secret settings (default backend, voice ID, output dir) go in agent-config.json.

Channel-plugin precedence (IMPORTANT)

A channel plugin that already transcribes audio (like the WhatsApp plugin with audio on) is authoritative for THAT channel. Do not call voice_transcribe on an audio file that arrived through such a plugin — you'd just be re-doing work the plugin already did, and you'd end up with two different transcriptions.

voice_transcribe is for:

  • Files from channels without built-in transcription (WebChat uploads, iMessage attachments)
  • Standalone files (the user dropped an .m4a in the workspace)
  • Cases where the channel plugin's transcription is disabled

Response style

  • /agent:voice status on CLI: full card.
  • On messaging channels: compact — single line per backend, skip the detailed reasons.
  • /agent:voice setup: step-by-step, one instruction at a time. Don't dump all 7 steps — guide the user through what's missing for their situation.

Never

  • Don't write API keys to agent-config.json — they're env-only.
  • Don't auto-run brew install — always let the user do it.
  • Don't transcribe WhatsApp voice notes with voice_transcribe if the plugin has audio-on.

References

  • docs/voice.md — full doc (backends, precedence, secrets, troubleshooting)
  • lib/voice.ts — routing + backends
  • skills/skill-manager/SKILL.md — for installing the sag skill from OpenClaw path

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free