Generate spectrogram and feature-panel visualizations from audio files.
npx skills add https://github.com/kcns008/clusterclaw --skill songsee-kcns008
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/kcns008/clusterclaw/tree/main/skills/songsee Command: npx skills add https://github.com/kcns008/clusterclaw --skill songsee-kcns008
This Skill helps you inspect audio visually instead of manually guessing what is happening in a track, making it easier to identify tonal content, rhythm, timbre, and changes over time.
Use the songsee skill to generate a spectrogram and feature panel image for the attached audio file.
High-intent search queries and answers about installing and using this skill.
To generate an audio spectrogram, use the songsee CLI to process common audio formats and export a visual frequency-time image. It renders clear spectrogram representations for quick inspection of tonal content and changes over time.
Yes, you can visualize audio features like chroma, MFCC, loudness, and tempogram. The tool renders multi-panel feature visualizations from audio files, enabling deeper inspection of rhythm and timbre alongside the spectrogram.
You can analyze a specific time slice by specifying a start time and duration in the CLI. This flexible audio slicing focuses the spectrogram and feature panels on a targeted section, useful for debugging problem segments.
Yes, the visualization tool supports optional ffmpeg decoding and accepts input from files or stdin. This allows configurable visualizations and image export directly within command-line workflows.
The best way to export audio feature panels as an image is using the songsee CLI. It generates clean multi-panel outputs including chroma and loudness, which can be exported directly for review or reporting.
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