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Songsee

Skill kevinnft/ai-agent-skills/skills/media/songsee

Audio spectrograms/features (mel, chroma, MFCC) via CLI.From its SKILL.md

Install
npx -y skills add kevinnft/ai-agent-skills --skill songsee

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SKILL.md

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songsee

Generate spectrograms and multi-panel audio feature visualizations from audio files.

Prerequisites

Requires Go:

go install github.com/steipete/songsee/cmd/songsee@latest

Optional: ffmpeg for formats beyond WAV/MP3.

Quick Start

# Basic spectrogram
songsee track.mp3

# Save to specific file
songsee track.mp3 -o spectrogram.png

# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux

# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg

# From stdin
cat track.mp3 | songsee - --format png -o out.png

Visualization Types

Use --viz with comma-separated values:

TypeDescription
spectrogramStandard frequency spectrogram
melMel-scaled spectrogram
chromaPitch class distribution
hpssHarmonic/percussive separation
selfsimSelf-similarity matrix
loudnessLoudness over time
tempogramTempo estimation
mfccMel-frequency cepstral coefficients
fluxSpectral flux (onset detection)

Multiple --viz types render as a grid in a single image.

Common Flags

FlagDescription
--vizVisualization types (comma-separated)
--styleColor palette: classic, magma, inferno, viridis, gray
--width / --heightOutput image dimensions
--window / --hopFFT window and hop size
--min-freq / --max-freqFrequency range filter
--start / --durationTime slice of the audio
--formatOutput format: jpg or png
-oOutput file path

Notes

  • WAV and MP3 are decoded natively; other formats require ffmpeg
  • Output images can be inspected with vision_analyze for automated audio analysis
  • Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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