Agent Skills: songsee

Audio spectrograms/features (mel, chroma, MFCC) via CLI.

UncategorizedID: PoorRican/dotfiles/songsee

Install this agent skill to your local

pnpm dlx add-skill https://github.com/PoorRican/dotfiles/tree/HEAD/configs/agent-skills/default/skills/media/songsee

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configs/agent-skills/default/skills/media/songsee/SKILL.md

Skill Metadata

Name
songsee
Description
"Audio spectrograms/features (mel, chroma, MFCC) via CLI."

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:

| Type | Description | |------|-------------| | spectrogram | Standard frequency spectrogram | | mel | Mel-scaled spectrogram | | chroma | Pitch class distribution | | hpss | Harmonic/percussive separation | | selfsim | Self-similarity matrix | | loudness | Loudness over time | | tempogram | Tempo estimation | | mfcc | Mel-frequency cepstral coefficients | | flux | Spectral flux (onset detection) |

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

Common Flags

| Flag | Description | |------|-------------| | --viz | Visualization types (comma-separated) | | --style | Color palette: classic, magma, inferno, viridis, gray | | --width / --height | Output image dimensions | | --window / --hop | FFT window and hop size | | --min-freq / --max-freq | Frequency range filter | | --start / --duration | Time slice of the audio | | --format | Output format: jpg or png | | -o | Output 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