How do I install the Songsee skill?

Run npx skillmds add thedixitjain/songsee in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Songsee skill do?

Audio spectrograms/features (mel, chroma, MFCC) via CLI. It is listed under Coding & Dev Tools on SkillMD.

Is Songsee safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Songsee?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Songsee free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Songsee?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Songsee by thedixitjain — Agent Skill

Name: Songsee
Rating: 4.2 (5 reviews)
Author: thedixitjain

File contents

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

Source: NousResearch/hermes-agent → skills/media/songsee/SKILL.md

---name: songseedescription: Audio spectrograms/features (mel, chroma, MFCC) via CLI.---# songseeGenerate spectrograms and multi-panel audio feature visualizations from audio files.## PrerequisitesRequires [Go](https://go.dev/doc/install):```bashgo install github.com/steipete/songsee/cmd/songsee@latest```Optional: `ffmpeg` for formats beyond WAV/MP3.## Quick Start```bash# Basic spectrogramsongsee track.mp3# Save to specific filesongsee track.mp3 -o spectrogram.png# Multi-panel visualization gridsongsee 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 stdincat track.mp3 | songsee - --format png -o out.png```## Visualization TypesUse `--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---**Source:** [`NousResearch/hermes-agent`](https://github.com/NousResearch/hermes-agent) → `skills/media/songsee/SKILL.md`

Frequently asked questions