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.
npx skills add StarryCod/cogitum --skill songseegit clone --depth 1 https://github.com/StarryCod/cogitumWrote 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.
[](https://agentmods.dev/skills/starrycod/cogitum/songsee)<a href="https://agentmods.dev/skills/starrycod/cogitum/songsee"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/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.
<a href="https://agentmods.dev/skills/starrycod/cogitum/songsee"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/songsee.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00020 | $0.00648 |
| Opus 5.5 | $0.00008 | $0.00259 |
| Sonnet 5.5 | $0.00004 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
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 1mo 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.
This is a copy
94% identical to songsee — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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_analyzefor automated audio analysis - Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines
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.
- 1mo ago First seen · 84 lines · 20 tokens per session scan A 61a9fd39d0af
songsee is a skill published in the GitHub repository StarryCod/cogitum (12 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 648 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to songsee, differing in 2 lines, and is treated as a copy.
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