elevenlabs

digitalsamba/claude-code-video-toolkit · updated Apr 8, 2026

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$npx skills add https://github.com/digitalsamba/claude-code-video-toolkit --skill elevenlabs
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summary

Requires ELEVENLABS_API_KEY in .env.

skill.md

ElevenLabs Audio Generation

Requires ELEVENLABS_API_KEY in .env.

Text-to-Speech

from elevenlabs.client import ElevenLabs
from elevenlabs import save, VoiceSettings
import os

client = ElevenLabs(api_key=os.getenv("ELEVENLABS_API_KEY"))

audio = client.text_to_speech.convert(
    text="Welcome to my video!",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    voice_settings=VoiceSettings(
        stability=0.5,
        similarity_boost=0.75,
        style=0.5,
        speed=1.0
    )
)
save(audio, "voiceover.mp3")

Models

Model Quality SSML Support Notes
eleven_multilingual_v2 Highest consistency None Stable, production-ready, 29 languages
eleven_flash_v2_5 Good <break>, <phoneme> Fast, supports pause/pronunciation tags
eleven_turbo_v2_5 Good <break>, <phoneme> Fastest latency
eleven_v3 Most expressive None Alpha — unreliable, needs prompt engineering

Choose: multilingual_v2 for reliability, flash/turbo for SSML control, v3 for maximum expressiveness (expect retakes).

Voice Settings by Style

Style stability similarity style speed
Natural/professional 0.75-0.85 0.9 0.0-0.1 1.0
Conversational 0.5-0.6 0.85 0.3-0.4 0.9-1.0
Energetic/YouTuber 0.3-0.5 0.75 0.5-0.7 1.0-1.1

Pauses Between Sections

With flash/turbo models: Use SSML break tags inline:

...end of section. <break time="1.5s" /> Start of next...

Max 3 seconds per break. Excessive breaks can cause speed artifacts.

With multilingual_v2 / v3: No SSML support. Options:

  • Paragraph breaks (blank lines) — creates ~0.3-0.5s natural pause
  • Post-process with ffmpeg: split audio and insert silence

WARNING: ... (ellipsis) is NOT a reliable pause — it can be vocalized as a word/sound. Do not use ellipsis as a pause mechanism.

Pronunciation Control

Phonetic spelling (any model): Write words as you want them pronounced:

  • JanusJan-us
  • nginxengine-x
  • Use dashes, capitals, apostrophes to guide pronunciation

SSML phoneme tags (flash/turbo only):

<phoneme alphabet="ipa" ph="ˈdʒeɪnəs">Janus</phoneme>

Iterative Workflow

  1. Generate → listen → identify pronunciation/pacing issues
  2. Adjust: phonetic spellings, break tags, voice settings
  3. Regenerate. If pauses aren't precise enough, add silence in post with ffmpeg rather than fighting the TTS engine.

Voice Cloning

Instant Voice Clone

with open("sample.mp3", "rb") as f:
    voice = client.voices.ivc.create(
        name="My Voice",
        files=[f],
        remove_background_noise=True
    )
print(f"Voice ID: {voice.voice_id}")
  • Use client.voices.ivc.create() (not client.voices.clone())
  • Pass file handles in binary mode ("rb"), not paths
  • Convert m4a first: ffmpeg -i input.m4a -codec:a libmp3lame -qscale:a 2 output.mp3
  • Multiple samples (2-3 clips) improve accuracy
  • Save voice ID for reuse

Professional Voice Clone: Requires Creator plan+, 30+ min audio. See reference.md.

Sound Effects

Max 22 seconds per generation.

result = client.text_to_sound_effects.convert(
    text="Thunder rumbling followed by heavy rain",
    duration_seconds=10,
    prompt_influence=0.3
)
with open("thunder.mp3", "wb") as f:
    for chunk in result:
        f.write(chunk)

Prompt tips: Be specific — "Heavy footsteps on wooden floorboards, slow and deliberate, with creaking"

Music Generation

10 seconds to 5 minutes. Use client.music.compose() (not .generate()).

result = client.music.compose(
    prompt="Upbeat indie rock, catchy guitar riff, energetic drums, travel vlog",
    music_length_ms=60000,
    force_instrumental=True
)
with open("music.mp3", "wb") as f:
    for chunk in result:
        f.write(chunk)

Prompt structure: Genre, mood, instruments, tempo, use case. Add "no vocals" or use force_instrumental=True for background music.

Remotion Integration

Complete Workflow: Script to Synchronized Scene

VOICEOVER-SCRIPT.md → voiceover.py → public/audio/ → Remotion composition
        ↓                  ↓               ↓                 ↓
  Scene narration    Generate MP3    Audio files     <Audio> component
  with durations     per scene       with timing     synced to scenes

Step 1: Generate Per-Scene Audio

Use the toolkit's voiceover tool to generate audio for each scene:

# Generate voiceover files for each scene
python tools/voiceover.py --scene-dir public/audio/scenes --json

# Output:
# public/audio/scenes/
#   ├── scene-01-title.mp3
#   ├── scene-02-problem.mp3
#   ├── scene-03-solution.mp3
#   └── manifest.json  (durations for each file)

The manifest.json contains timing info:

{
  "scenes": [
    { "file": "scene-01-title.mp3", "duration": 4.2 },
    { "file": "scene-02-problem.mp3", "duration": 12.8 },
    { "file": "scene-03-solution.mp3", "duration": 15.3 }
  ],
  "totalDuration": 32.3
}

Step 2: Use Audio in Remotion Composition

// src/Composition.tsx
import { Audio, staticFile, Series, useVideoConfig } from 'remotion';

// Import scene components
import { TitleSlide } from './scenes/TitleSlide';
import { ProblemSlide } from './scenes/ProblemSlide';
import { SolutionSlide } from './scenes/SolutionSlide';

// Scene durations (from manifest.json, converted to frames at 30fps)
const SCENE_DURATIONS = {
  title: Math.ceil(4.2 * 30),      // 126 frames
  problem: Math.ceil(12.8 * 30),   // 384 frames
  solution: Math.ceil(15.3 * 30),  // 459 frames
};

export const MainComposition: React.FC = () => {
  return (
    <>
      {/* Scene sequence */}
      <Series>
        <Series.Sequence durationInFrames={SCENE_DURATIONS.title}>
          <TitleSlide />
        </Series.Sequence>
        <Series.Sequence durationInFrames={SCENE_DURATIONS.problem}>
          <ProblemSlide />
        
how to use elevenlabs

How to use elevenlabs on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add elevenlabs
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/digitalsamba/claude-code-video-toolkit --skill elevenlabs

The skills CLI fetches elevenlabs from GitHub repository digitalsamba/claude-code-video-toolkit and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/elevenlabs

Reload or restart Cursor to activate elevenlabs. Access the skill through slash commands (e.g., /elevenlabs) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • 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

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.649 reviews
  • Yuki Dixit· Dec 28, 2024

    elevenlabs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Dec 12, 2024

    I recommend elevenlabs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Layla Singh· Dec 12, 2024

    elevenlabs reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Advait Haddad· Dec 4, 2024

    Useful defaults in elevenlabs — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Michael Johnson· Nov 23, 2024

    I recommend elevenlabs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yuki Martin· Nov 19, 2024

    Solid pick for teams standardizing on skills: elevenlabs is focused, and the summary matches what you get after install.

  • Layla Zhang· Nov 15, 2024

    Keeps context tight: elevenlabs is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sakshi Patil· Nov 3, 2024

    Useful defaults in elevenlabs — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Liam Diallo· Nov 3, 2024

    elevenlabs has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 22, 2024

    elevenlabs has been reliable in day-to-day use. Documentation quality is above average for community skills.

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