| name | music-creative |
| description | Music creative suite: AI music generation (Suno/HeartMuLa), audio analysis (songsee), and songwriting craft. |
| version | 1.0.0 |
| author | Hermes Agent |
| license | MIT |
| platforms | ["linux","macos","windows"] |
| metadata | {"hermes":{"tags":["music","songwriting","audio-analysis","AI-music","Suno"]}} |
Music Creative Suite
Unified skill for all music-related creative workflows. Three modes: AI music generation, audio feature analysis, and songwriting craft.
Section A: AI Music Generation (HeartMuLa + Suno)
Generate music from lyrics + tags using HeartMuLa (local) or Suno API.
HeartMuLa
Local song generation from lyrics and style tags. Use when you need full control over generation without external API dependencies.
Workflow: Provide lyrics + genre/style tags → generate audio → deliver to user.
Suno AI Music
Use the songwriting-and-ai-music skill for Suno AI prompts and workflows.
Key conventions:
- Lyrics should be structured (verse, chorus, bridge)
- Style tags control genre, mood, instrumentation
- Always specify tempo, key, and instrumentation preferences
- Suno generates full songs (~2-4 minutes)
Section B: Audio Analysis (songsee)
Extract audio spectrograms and features (mel, chroma, MFCC) via CLI.
Use when: User asks about audio characteristics, wants to analyze a song's structure, or needs feature extraction for ML/audio ML tasks.
Features:
- Mel spectrograms: frequency-time representation
- Chroma features: pitch class energy over time
- MFCC: timbral characteristics
- Beat detection and tempo estimation
Section C: Songwriting Craft
Songwriting craft guidance and AI music prompts.
Key principles:
- Structure matters: verse-chorus-verse-chorus-bridge-chorus is standard
- Lyrics should match the rhythm and mood of the intended style
- Hook/chorus should be memorable and repeatable
- Bridge provides contrast before final chorus
- For AI generation, be explicit about style, mood, instrumentation, and structure in prompts
Pitfalls
- AI music generation is stochastic — multiple generations may be needed for quality results
- Lyrics structure affects output — unstructured text produces unstructured music
- Style tags are powerful — be specific about genre, mood, era, instrumentation
- Audio analysis requires the actual audio file — cannot analyze from text description alone
