aviz85/claude-skills-library/quick-voice

quick-voice

White-label voice session generator. Each invocation produces a per-session web app that opens a WebRTC voice channel to OpenAI Realtime (gpt-realtime-2) with context-specific instructions, tools, and canvas behavior.

When to use

• "תפעיל איתי שיחה קולית על X" / "let's have a quick voice call about X"
• Going through a list of items where speaking is faster than reading + clicking
• Closing a topic that needs a few decisions + updates (not a long-form plan)
• Producing a structured output (decisions / action items / notes) from a free-form discussion

Two modes

The canvas is available in both modes.

How to run a session

Step 1 — figure out context

Look at the conversation. The user said one of:

• Explicit: "let's close the freelancer reviews" → topic = freelancer reviews
• Implicit: earlier in the conversation we generated 10 images → topic = "go over generated images"
• Vague: "תפעיל שיחה קולית" → call AskUserQuestion (single Q) to clarify topic + mode

Step 2 — pick a runtime directory + generate the session config

Each session has its own runtime directory holding config.json, output.md, server.log, done.flag. The runtime dir lives outside the skill so the skill itself stays code-only and project data stays with the project.

Pick a session id: id=$(date +%Y%m%d-%H%M%S).

Choose the runtime directory:

• Inside a project (git repo / codebase you're working in):

put it at <project-root>/.quick-voice/<id>/. Add .quick-voice/ to the project's .gitignore so session data never gets committed.

• No project context:

use /tmp/quick-voice-$USER/<id>/.

Create the directory and write config.json into it:

{
  "mode": "live",
  "topic": "Short Hebrew topic title",
  "instructions": "Full Hebrew system prompt for the realtime agent. Tell it what to do, what to ask, when to use the canvas, when to call save_note, when to call end_session. Be specific about the flow.",
  "voice": "ash",
  "cwd": "/absolute/path/used/as/root/for/relative/file/ops",
  "tools": ["canvas_show", "canvas_clear", "save_note", "end_session", "read_file", "list_dir", "update_json"],
  "canvas_hints": [
    { "type": "image", "source": "/abs/path/to/image1.png", "title": "Image 1" },
    { "type": "image", "source": "/abs/path/to/image2.png", "title": "Image 2" }
  ],
  "output_template": "# Session output\n\n## Decisions\n\n## Action items\n\n## Notes\n"
}

Fields:

• mode: "live" or "distill".
• topic: shown in the page header.
• instructions: the system prompt. Write it in Hebrew (Aviz prefers Hebrew). Be specific — describe the flow you want the agent to follow.
• voice: "ash" (default), "alloy", "cedar", etc.
• cwd: directory the file tools are scoped to. Required if any file tool is enabled.
• tools: whitelist of tool names from the full set (see lib/tool-defs.js). For distill mode use only: canvas_show, canvas_clear, save_note, end_session. For live mode add file / JSON / bash tools as needed.
• canvas_hints: optional. If you pre-load items the agent should walk through, list them here. Otherwise the agent decides what to show.
• output_template: seeds output.md so the agent has a structure to fill in via save_note.

Step 3 — launch

node ~/.claude/skills/quick-voice/scripts/launch.js <runtime-dir>

<runtime-dir> is the absolute path to the directory you created in Step 2. The launcher reads <runtime-dir>/config.json and writes output.md, server.log, done.flag back into the same directory.

Cross-platform (macOS / Linux / Windows). This:

1. Verifies OPENAI_API_KEY (from env or ~/.claude/skills/quick-voice/.env)
2. Runs npm install once if node_modules is missing
3. Finds a free port in 3031-3040 (uses net.createServer — no shell needed)
4. Spawns server.js, polls /config until ready
5. Opens the default browser at http://localhost:<port> (open on macOS, xdg-open on Linux, start on Windows)
6. Waits for the user to end the session (close browser → /done is hit, or the agent calls end_session)
7. Prints output.md and exits

Step 4 — surface the output

After the launcher returns, read runtime/<id>/output.md and present it to the user. Do NOT delete the runtime dir automatically — the user may want to re-open or audit it. The session log is in runtime/<id>/server.log.

Available tools (full set)

See lib/tool-defs.js for OpenAI Realtime tool definitions and lib/tools.js for implementations. Whitelist via config.json.tools.

Canvas (both modes):

• canvas_show({ type, source, title?, content? }) — display in canvas. type ∈ image|markdown|html|code|json|video|audio|text|url.
• For media (image, video, audio): pass source (file path or URL).
• For text-like (markdown, html, code, json, text): pass either content (inline string) OR source (file path — the client fetches the file via /file and renders it). If you have a long block already on disk, prefer source; if you're generating short content inline, use content.
• For url: pass source (iframe src).
• canvas_clear() — clear canvas.

Output / control (both modes):

• save_note({ heading, content }) — append a section to output.md.
• end_session({ summary? }) — finalize and close. summary is appended to output.md.

Data (live mode only):

• read_file({ path }) — read file under cwd.
• write_file({ path, content }) — write file under cwd.
• append_file({ path, content }) — append.
• update_json({ path, patch }) — shallow-merge patch into a JSON file (object root only).
• list_dir({ path }) — list directory contents.
• run_bash({ cmd }) — run a shell command in cwd. Use sparingly.

Examples

Example 1 — "let's go over the images you just created" (distill)

{
  "mode": "distill",
  "topic": "סקירת תמונות",
  "instructions": "אתה מציג למשתמש תמונות אחת אחת. עבור כל תמונה: 1) קרא ל-canvas_show עם הנתיב מ-canvas_hints, 2) שאל 'מה דעתך?', 3) הקשב לתגובה, 4) קרא ל-save_note עם heading='[שם תמונה]' ו-content=[תגובת המשתמש]. כשמסיימים את כל התמונות — קרא ל-end_session.",
  "voice": "ash",
  "tools": ["canvas_show", "canvas_clear", "save_note", "end_session"],
  "canvas_hints": [
    { "type": "image", "source": "/Users/aviz/aviz-crm/output/img-001.png", "title": "1" },
    { "type": "image", "source": "/Users/aviz/aviz-crm/output/img-002.png", "title": "2" }
  ],
  "output_template": "# פידבק על תמונות\n\n"
}

Example 2 — "review pending freelancer scores" (live)

{
  "mode": "live",
  "topic": "סקירת פרילנסרים",
  "instructions": "פתח בקריאה ל-read_file({path: 'data/freelancers.json'}). הצג כל פרילנסר בקנבס (canvas_show type=json). שאל את אביץ לעדכון ציון. עדכן עם update_json. תעד ב-save_note. סיים עם end_session.",
  "voice": "ash",
  "cwd": "/Users/aviz/aviz-crm",
  "tools": ["canvas_show", "canvas_clear", "save_note", "end_session", "read_file", "update_json", "list_dir"],
  "output_template": "# Freelancer review session\n\n## Updates made\n\n"
}

Example 3 — vague invocation

User says only "תפעיל שיחה קולית". Call AskUserQuestion once:

Question: "על מה השיחה?"

Options: [explicit topic the user types in via 'Other'], "סקירה חופשית — distill בלבד"

Then build the config from the answer.

Anti-patterns

• Don't bake secrets. OPENAI_API_KEY comes from ~/.claude/skills/quick-voice/.env (or the project's .env). Never inline.
• Don't generate huge instructions. Keep instructions ≤ 2KB. The agent needs to act fast.
• Don't skip `cwd` when enabling file tools — it scopes the blast radius.
• Don't enable `run_bash` unless really needed. Prefer specific tools.
• Don't auto-delete `runtime/<id>/`. The user may want to re-open or audit.

Files in this skill

• server.js — Express server. Reads $QV_RUNTIME_DIR/config.json, vends OpenAI ephemeral tokens, executes tool calls server-side, serves files via /file?path=....
• public/index.html, public/app.js — WebRTC client + generic canvas renderer (markdown/html/code/json/text can be loaded from source paths in addition to inline content).
• lib/tool-defs.js — OpenAI Realtime tool schemas.
• lib/tools.js — server-side tool implementations.
• scripts/launch.js — cross-platform Node launcher: takes an absolute <runtime-dir> path, finds free port, spawns server, opens browser, waits for done.

Per-session files (config.json, output.md, server.log, done.flag) live in the runtime directory you picked — typically <project>/.quick-voice/<id>/ or /tmp/quick-voice-$USER/<id>/.