songsee

Generate spectrograms and feature panels from audio files using songsee CLI.

3|Updated Mar 15, 2026
One-click install
npx skills add https://github.com/ai-in-pm/NemoClawd --skill songsee-ai-in-pm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/ai-in-pm/NemoClawd/tree/main/apps/clawdbot-main/skills/songsee
Command: npx skills add https://github.com/ai-in-pm/NemoClawd --skill songsee-ai-in-pm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires songsee, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the need for audio analysis by generating spectrograms and feature-panel visualizations, making audio data more accessible and understandable.

Core Features & Use Cases

  • Spectrogram Generation: Converts audio signals into visual representations.
  • Feature Panel Visualizations: Provides insights into audio features such as loudness, pitch, and rhythm.
  • Use Case: Use this Skill to visualize the audio spectrum of a music track and analyze its characteristics.

Quick Start

Generate a spectrogram for an audio file 'track.mp3'.

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 for music analysis?

To generate a spectrogram for music analysis, you need an audio file like 'track.mp3' and the songsee CLI tool. The Skill processes the audio signal through the command line to produce a visual representation of the audio spectrum.

What audio features can I visualize when analyzing a music track?

When analyzing a music track, you can visualize audio features such as loudness, pitch, and rhythm. The Skill generates feature panel visualizations alongside spectrograms to provide comprehensive insights into audio characteristics.

Do I need the songsee CLI to visualize audio data and feature panels?

Yes, you need the songsee CLI installed to process audio files into visual formats. The Skill relies on this command-line dependency to convert audio signals into spectrograms and feature panel visualizations.

What's the best way to analyze audio characteristics for sound research?

The best way to analyze audio characteristics for sound research is by generating both spectrograms and feature panels. This approach converts audio signals into visual representations, making complex audio data more accessible and understandable for research purposes.

Can I visualize the audio spectrum of any music track format?

You can visualize the audio spectrum of music tracks using this Skill, which processes audio files into visual formats. It generates spectrograms and feature panels ideal for audio engineering and music analysis, though specific supported formats depend on the songsee CLI capabilities.