Fetches voicebox-voice-synthesis from aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/voicebox-voice-synthesis
Restart Cursor to activate voicebox-voice-synthesis. Access via /voicebox-voice-synthesis in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Voicebox is a local-first, open-source voice cloning and TTS studio — a self-hosted alternative to ElevenLabs. It runs entirely on your machine (macOS MLX/Metal, Windows/Linux CUDA, CPU fallback), exposes a REST API on localhost:17493, and ships with 5 TTS engines, 23 languages, post-processing effects, and a multi-track Stories editor.
git clone https://github.com/jamiepine/voicebox.git
cd voicebox
# Install just task runnerbrew install just # macOScargoinstall just # any platform# Set up Python venv + all dependenciesjust setup
# Start backend + desktop app in dev modejust dev
# List all available commandsjust --list
Architecture
Layer
Technology
Desktop App
Tauri (Rust)
Frontend
React + TypeScript + Tailwind CSS
State
Zustand + React Query
Backend
FastAPI (Python) on port 17493
TTS Engines
Qwen3-TTS, LuxTTS, Chatterbox, Chatterbox Turbo, TADA
Effects
Pedalboard (Spotify)
Transcription
Whisper / Whisper Turbo
Inference
MLX (Apple Silicon) / PyTorch (CUDA/ROCm/XPU/CPU)
Database
SQLite
The Python FastAPI backend handles all ML inference. The Tauri Rust shell wraps the frontend and manages the backend process lifecycle. The API is accessible directly at http://localhost:17493 even when using the desktop app.
REST API Reference
Base URL: http://localhost:17493
Interactive docs: http://localhost:17493/docs
Generate Speech
# Basic generationcurl-X POST http://localhost:17493/generate \-H"Content-Type: application/json"\-d'{
"text": "Hello world, this is a voice clone.",
"profile_id": "abc123",
"language": "en"
}'# With engine selectioncurl-X POST http://localhost:17493/generate \-H"Content-Type: application/json"\-d'{
"text": "Speak slowly and with gravitas.",
"profile_id": "abc123",
"language": "en",
"engine": "qwen3-tts"
}'# With paralinguistic tags (Chatterbox Turbo only)curl-X POST http://localhost:17493/generate \-H"Content-Type: application/json"\-d'{
"text": "That is absolutely hilarious! [laugh] I cannot believe it.",
"profile_id": "abc123",
"engine": "chatterbox-turbo",
"language": "en"
}'
Voice Profiles
# List all profilescurl http://localhost:17493/profiles
# Create a new profilecurl-X POST http://localhost:17493/profiles \-H"Content-Type: application/json"\-d'{
"name": "Narrator",
"language": "en",
"description": "Deep narrative voice"
}'# Upload audio sample to a profilecurl-X POST http://localhost:17493/profiles/{profile_id}/samples \-F"file=@/path/to/voice-sample.wav"# Export a profilecurl http://localhost:17493/profiles/{profile_id}/export \--output narrator-profile.zip
# Import a profilecurl-X POST http://localhost:17493/profiles/import \-F"[email protected]"
Generation Queue & Status
# Get generation status (SSE stream)curl-N http://localhost:17493/generate/{generation_id}/status
# List recent generationscurl http://localhost:17493/generations
# Retry a failed generationcurl-X POST http://localhost:17493/generations/{generation_id}/retry
# Download generated audiocurl http://localhost:17493/generations/{generation_id}/audio \--output output.wav
Models
# List available models and download statuscurl http://localhost:17493/models
# Unload a model from GPU memory (without deleting)curl-X POST http://localhost:17493/models/{model_id}/unload
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
