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 ApiliumCode/mayros --skill songseegit clone --depth 1 https://github.com/ApiliumCode/mayrosWrote 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/apiliumcode/mayros/songsee)<a href="https://agentmods.dev/skills/apiliumcode/mayros/songsee"><img src="https://agentmods.dev/badge/skills/apiliumcode/mayros/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/apiliumcode/mayros/songsee"><img src="https://agentmods.dev/badge/skills/apiliumcode/mayros/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 | $0.00368 |
| Opus 5.5 | $0.00008 | $0.00147 |
| Sonnet 5.5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
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
100% identical to songsee — 4 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 + feature panels from audio.
Quick start
- Spectrogram:
songsee track.mp3 - Multi-panel:
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux - Time slice:
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg - Stdin:
cat track.mp3 | songsee - --format png -o out.png
Common flags
--vizlist (repeatable or comma-separated)--stylepalette (classic, magma, inferno, viridis, gray)--width/--heightoutput size--window/--hopFFT settings--min-freq/--max-freqfrequency range--start/--durationtime slice--formatjpg|png
Notes
- WAV/MP3 decode native; other formats use ffmpeg if available.
- Multiple
--vizrenders a grid.
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 · 50 lines · 20 tokens per session scan A 776979b1ce1e
songsee is a skill published in the GitHub repository ApiliumCode/mayros (13 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 368 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to songsee, differing in 4 lines, and is treated as a copy.
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