Fetches audiocraft-audio-generation from davila7/claude-code-templates 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/audiocraft-audio-generation
Restart Cursor to activate audiocraft-audio-generation. Access via /audiocraft-audio-generation 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.
Comprehensive guide to using Meta's AudioCraft for text-to-music and text-to-audio generation with MusicGen, AudioGen, and EnCodec.
When to use AudioCraft
Use AudioCraft when:
Need to generate music from text descriptions
Creating sound effects and environmental audio
Building music generation applications
Need melody-conditioned music generation
Want stereo audio output
Require controllable music generation with style transfer
Key features:
MusicGen: Text-to-music generation with melody conditioning
AudioGen: Text-to-sound effects generation
EnCodec: High-fidelity neural audio codec
Multiple model sizes: Small (300M) to Large (3.3B)
Stereo support: Full stereo audio generation
Style conditioning: MusicGen-Style for reference-based generation
Use alternatives instead:
Stable Audio: For longer commercial music generation
Bark: For text-to-speech with music/sound effects
Riffusion: For spectogram-based music generation
OpenAI Jukebox: For raw audio generation with lyrics
Quick start
Installation
# From PyPIpip install audiocraft
# From GitHub (latest)pip install git+https://github.com/facebookresearch/audiocraft.git
# Or use HuggingFace Transformerspip install transformers torch torchaudio
Basic text-to-music (AudioCraft)
import torchaudio
from audiocraft.models import MusicGen
# Load modelmodel = MusicGen.get_pretrained('facebook/musicgen-small')# Set generation parametersmodel.set_generation_params( duration=8,# seconds top_k=250, temperature=1.0)# Generate from textdescriptions =["happy upbeat electronic dance music with synths"]wav = model.generate(descriptions)# Save audiotorchaudio.save("output.wav", wav[0].cpu(), sample_rate=32000)
Using HuggingFace Transformers
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import scipy
# Load model and processorprocessor = AutoProcessor.from_pretrained("facebook/musicgen-small")model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")model.to("cuda")# Generate musicinputs = processor( text=["80s pop track with bassy drums and synth"], padding=True, return_tensors="pt").to("cuda")audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)# Savesampling_rate = model.config.audio_encoder.sampling_rate
scipy.io.wavfile.write("output.wav", rate=sampling_rate, data=audio_values[0,0].cpu().numpy())
Text-to-sound with AudioGen
from audiocraft.models import AudioGen
# Load AudioGenmodel = AudioGen.get_pretrained('facebook/audiogen-medium')model.set_generation_params(duration=5)# Generate sound effectsdescriptions =["dog barking in a park with birds chirping"]wav = model.generate(descriptions)torchaudio.save("sound.wav", wav[0].cpu(), sample_rate=16000)
from audiocraft.models import MusicGen
import torchaudio
model = MusicGen.get_pretrained('facebook/musicgen-medium')# Configure generationmodel.set_generation_params( duration=30,# Up to 30 seconds top_k=250,# Sampling diversity top_p=0.0,# 0 = use top_k only temperature=1.0,# Creativity (higher = more varied) cfg_coef=3.0# Text adherence (higher = stricter))# Generate multiple samplesdescriptions =["epic orchestral soundtrack with strings and brass","chill lo-fi hip hop beat with jazzy piano","energetic rock song with electric guitar"]# Generate (returns [batch, channels, samples])wav = model.generate(descriptions)# Save eachfor i, audio inenumerate(wav): torchaudio.save(f"music_{i}.wav", audio.cpu(), sample_rate=32000)
Melody-conditioned generation
from audiocraft.models import MusicGen
import torchaudio
# Load melody modelmodel = MusicGen.get_pretrained('facebook/musicgen-melody')model.set_generation_params(duration=30)# Load melody audiomelody, sr = torchaudio.load("melody.wav")# Generate with melody conditioningdescriptions =["acoustic guitar folk song"]wav = model.generate_with_chroma(descriptions, melody, sr)torchaudio.save("melody_conditioned.wav", wav[0].cpu(), sample_rate=32000)
Stereo generation
from audiocraft.models import MusicGen
# Load stereo modelmodel = MusicGen.get_pretrained('facebook/musicgen-stereo-medium')model.set_generation_params(duration=15)descriptions =["ambient electronic music with wide stereo panning"]wav = model.generate(descriptions)# wav shape: [batch, 2, samples] for stereo
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›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
