Ultra-fast text-to-speech with ~90ms latency and 8 built-in voices.
Works with
Delivers audio in approximately 90ms after daemon warmup, with first run taking 2-5 seconds for model initialization
Includes 8 pre-configured voices (alba, marius, javert, jean, fantine, cosette, eponine, azelma) accessible via simple command-line flags
Supports file output with configurable directory allowlisting, quiet mode, and UTF-8 text input including long-form content
Auto-starting daemon with 1-hour idle
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionspeakturbo-ttsExecute the skills CLI command in your project's root directory to begin installation:
Fetches speakturbo-tts from emzod/speak-turbo and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate speakturbo-tts. Access via /speakturbo-tts in your agent's command palette.
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.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
18
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
18
stars
Give your agent the ability to speak to you real-time. Ultra-fast text-to-speech with ~90ms latency and 8 built-in voices.
# Play immediately - you should hear "Hello world" through your speakers
speakturbo "Hello world"
# Output: ⚡ 92ms → ▶ 93ms → ✓ 1245ms
# Verify it's working by saving to file
speakturbo "Hello world" -o test.wav
ls -lh test.wav # Should show ~50-100KB file
Output explained: ⚡ = first audio received, ▶ = playback started, ✓ = done
The first execution takes 2-5 seconds while the daemon starts and loads the model into memory. Subsequent calls are ~90ms to first sound.
# First run (slow - daemon starting)
speakturbo "Starting up" # ~2-5 seconds
# Second run (fast - daemon already running)
speakturbo "Now I'm fast" # ~90ms
# Basic - plays immediately (default voice: alba)
speakturbo "Hello world"
# Save to file (no audio playback)
speakturbo "Hello" -o output.wav
# Save to specific file
speakturbo "Goodbye" -o goodbye.wav
# Quiet mode (suppress status messages, still plays audio)
speakturbo "Hello" -q
# List available voices
speakturbo --list-voices
| Voice | Type |
|---|---|
alba |
Female (default) |
marius |
Male |
javert |
Male |
jean |
Male |
fantine |
Female |
cosette |
Female |
eponine |
Female |
azelma |
Female |
| Metric | Value |
|---|---|
| Time to first sound | ~90ms (daemon warm) |
| First run | 2-5s (daemon startup) |
| Real-time factor | ~4x faster |
| Sample rate | 24kHz mono |
speakturbo (Rust CLI, 2.2MB)
│
│ HTTP streaming (port 7125)
▼
speakturbo-daemon (Python + pocket-tts)
│
│ Model in memory, auto-shutdown after 1hr idle
▼
Audio playback (rodio)
speakturbo "She said \"hello\""The -o flag only writes to directories that are on the allowlist. By default, these are:
/tmp and system temp directories~/.speakturbo/If you need to write elsewhere, use --allow-dir:
speakturbo "Hello" -o /custom/path/audio.wav --allow-dir /custom/path
To permanently allow a directory, add it to ~/.speakturbo/config:
mkdir -p ~/.speakturbo && echo "/custom/path" >> ~/.speakturbo/config
The config file is one directory per line. Lines starting with # are comments.
| Code | Meaning |
|---|---|
| 0 | Success (audio played/saved) |
| 1 | Error (daemon connection failed, invalid args) |
Use speakturbo when:
Use speak instead when:
speak "text" --voice ~/.chatter/voices/morgan_freeman.wav[laugh], [sigh]See the speak skill documentation for full usage.
No audio plays:
# Check daemon is running
curl http://127.0.0.1:7125/health
# Expected: {"status":"ready","voices":["alba","marius",...]}
# Verify by saving to file and playing manually
speakturbo "test" -o /tmp/test.wav
afplay /tmp/test.wav # macOS
aplay /tmp/test.wav # Linux
Daemon won't start:
# Check port availability
lsof -i :7125
# Manually kill and restart
pkill -f "daemon_streaming"
speakturbo "test" # Auto-restarts daemon
First run is slow: This is expected. The daemon needs to load the ~100MB model into memory. Subsequent calls will be fast (~90ms).
The daemon auto-starts on first use and auto-shuts down after 1 hour idle.
# Check status
curl http://127.0.0.1:7125/health
# Manual stop
pkill -f "daemon_streaming"
# View logs
cat /tmp/speakturbo.log
| Feature | speakturbo | speak |
|---|---|---|
| Time to first sound | ~90ms | ~4-8s |
| Voice cloning | ❌ | ✅ |
| Emotion tags | ❌ | ✅ |
| Voices | 8 built-in | Custom wav files |
| Engine | pocket-tts | Chatterbox |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
speakturbo-tts reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: speakturbo-tts is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for speakturbo-tts matched our evaluation — installs cleanly and behaves as described in the markdown.
speakturbo-tts fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for speakturbo-tts matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: speakturbo-tts is focused, and the summary matches what you get after install.
speakturbo-tts has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in speakturbo-tts — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in speakturbo-tts — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend speakturbo-tts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 74