Requires ELEVENLABS_API_KEY in .env.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionelevenlabsExecute the skills CLI command in your project's root directory to begin installation:
Fetches elevenlabs from digitalsamba/claude-code-video-toolkit 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 elevenlabs. Access via /elevenlabs 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
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Requires ELEVENLABS_API_KEY in .env.
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")
| 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).
| 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 |
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:
WARNING: ... (ellipsis) is NOT a reliable pause — it can be vocalized as a word/sound. Do not use ellipsis as a pause mechanism.
Phonetic spelling (any model): Write words as you want them pronounced:
Janus → Jan-usnginx → engine-xSSML phoneme tags (flash/turbo only):
<phoneme alphabet="ipa" ph="ˈdʒeɪnəs">Janus</phoneme>
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}")
client.voices.ivc.create() (not client.voices.clone())"rb"), not pathsffmpeg -i input.m4a -codec:a libmp3lame -qscale:a 2 output.mp3Professional Voice Clone: Requires Creator plan+, 30+ min audio. See reference.md.
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"
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.
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
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
}
// 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 />
✓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
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
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.6★★★★★49 reviews- YYuki Dixit★★★★★Dec 28, 2024
elevenlabs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- PPratham Ware★★★★★Dec 12, 2024
I recommend elevenlabs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- LLayla Singh★★★★★Dec 12, 2024
elevenlabs reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAdvait Haddad★★★★★Dec 4, 2024
Useful defaults in elevenlabs — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- MMichael Johnson★★★★★Nov 23, 2024
I recommend elevenlabs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- YYuki Martin★★★★★Nov 19, 2024
Solid pick for teams standardizing on skills: elevenlabs is focused, and the summary matches what you get after install.
- LLayla 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.
- SSakshi Patil★★★★★Nov 3, 2024
Useful defaults in elevenlabs — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- LLiam Diallo★★★★★Nov 3, 2024
elevenlabs has been reliable in day-to-day use. Documentation quality is above average for community skills.
- CChaitanya 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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