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Skill

/songsee

Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.

BOOST
From plugin
hermes-desktop
17790 skills
Install
$ npx -y skills add redwoodog/hermes-desktop --skill songsee --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/songsee

Context preview

The summary Claude sees to decide when to auto-load this skill.

Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.

SKILL.md

songsee.SKILL.md
name: songsee
description: Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
version: 1.0.0
author: community
license: MIT
metadata:
  hermes:
    tags: [Audio, Visualization, Spectrogram, Music, Analysis]
    homepage: https://github.com/steipete/songsee
prerequisites:
  commands: [songsee]

songsee

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

Prerequisites

Requires [Go](https://go.dev/doc/install):

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
Read more
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