Music audio workflows
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Umbrella workflow for songwriting, AI music generation, and audio feature visualization/analysis.
SKILL.md
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Music and Audio Workflows
Use this umbrella when creating songs, prompting AI music systems, generating Suno-like tracks, or analyzing audio with spectrogram/features.
Choose the workflow
| Intent | Pattern |
|---|---|
| Write lyrics or improve a song | Songwriting craft: concept, structure, hook, imagery, prosody. |
| Generate a track from lyrics/tags | HeartMuLa-style AI music generation: prepare lyrics, tags, then render. |
| Inspect or visualize audio | Songsee-style spectrogram/features: mel, chroma, MFCC, waveform, beat cues. |
Songwriting craft
Start with a clear premise, singer perspective, emotional turn, and form. Check hook memorability, stress patterns, rhyme density, concrete imagery, and whether each section advances the song.
AI music prompt preparation
Separate the artistic brief from generation inputs:
- Lyrics: complete sections with labels like
[Verse],[Chorus],[Bridge]when supported. - Tags: genre, tempo, instrumentation, vocal style, mood, production era.
- Negative constraints: what to avoid, if the generator supports them.
Verify generated files exist and can be played before claiming success.
Audio analysis
Use feature extraction when the user asks what is happening in audio, needs visual assets, or wants to compare mixes. Return both high-level interpretation and the artifact path when producing images/audio-derived files.