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songsee skill

by kevinnft·kevinnft/ai-agent-skills·14 stars·MIT

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

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Install the songsee skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/kevinnft/ai-agent-skills.git /tmp/ai-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/ai-agent-skills/skills/media/songsee ~/.claude/skills/songsee
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

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

Common Flags

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

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