songsee

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

1|1|Updated Apr 25, 2026
One-click install
npx skills add https://github.com/linfordWu/owls --skill songsee-linfordwu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/linfordWu/owls/tree/main/skills/media/songsee
Command: npx skills add https://github.com/linfordWu/owls --skill songsee-linfordwu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Turning raw audio data into visual representations to support quick analysis, debugging, and documentation.

Core Features & Use Cases

  • Spectrogram generation for standard frequency visualization.
  • Multi-panel visualizations (mel, chroma, MFCC, tempogram, etc.) for in-depth analysis.
  • Use cases include audio analysis, music production debugging, and visual documentation of audio projects.

Quick Start

Run songsee track.mp3 to generate a spectrogram image and visualizations.

Frequently Asked Questions about songsee

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a spectrogram from an audio file?

To generate a spectrogram from an audio file, run the command line interface by passing your track as an argument. This creates visual representations of the audio data for quick analysis and debugging.

What audio features can I visualize for music production debugging?

For music production debugging, you can visualize multiple audio features including mel, chroma, MFCC, and tempograms. These multi-panel visualizations support in-depth analysis of your audio projects.

Do I need a Go toolchain to visualize audio features?

Yes, you need the Go toolchain installed to use this CLI for audio feature visualization. The command line tool requires this environment to process audio files and generate spectrogram images.

Can I use ffmpeg to support broader audio formats for spectrogram generation?

Yes, ffmpeg provides optional support for broader audio formats during spectrogram generation. Integrating it allows the CLI to process a wider variety of input files beyond standard formats.

What is the best way to create visual documentation for audio analysis?

The best way to create visual documentation for audio analysis is generating multi-panel visualizations like spectrograms and MFCCs. This turns raw audio data into images that support quick project documentation.