songsee

songsee is a skill for Claude Code, Codex from NousResearch/hermes-agent. It costs 20 tokens per session (648 once invoked), scanned A, original, MIT.

A command-line tool that turns audio into spectrograms and visual charts of properties such as pitch, loudness, rhythm, and sound texture.

In plain words
What is it for?
Use it to inspect recordings, compare audio sections, and create visual analyses from music or other sound files.
Why use it?
It makes patterns in an audio file visible, which can be difficult to inspect by listening alone.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to inspect recordings, compare audio sections, and create visual analyses from music or other sound files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nousresearch/hermes-agent/songsee
About the project

Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.

NousResearch/hermes-agent · 243,598 stars · on GitHub · hermes-agent.nousresearch.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add NousResearch/hermes-agent --skill songsee
Clone the repo
git clone --depth 1 https://github.com/NousResearch/hermes-agent

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for songsee

README.md
[![agentmods](https://agentmods.dev/badge/skills/nousresearch/hermes-agent/songsee/github.svg)](https://agentmods.dev/skills/nousresearch/hermes-agent/songsee)
Your own site
<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/songsee"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/songsee/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for songsee

Your own site · 80×15
<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/songsee"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/songsee.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 648 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk pass 7 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00020 $0.00648
Opus 5 $0.00010 $0.00324
Sonnet 5 $0.00004 $0.00130
Haiku 4.5 $0.00002 $0.00065

Measured 6d ago against content hash d9d8d1394c63, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

songsee scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
  • songsee — 100% identical, 0 lines differ
skills/media/songsee/SKILL.md · 84 lines

What it actually says

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
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 84 lines · 20 tokens per session scan A d9d8d1394c63

Subscribe to this mod's changes

songsee is a skill published in the GitHub repository NousResearch/hermes-agent (243,598 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 648 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.