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 agentmods add skills/john-data-chen/hermes-agent-backup/songseenpx skills add john-data-chen/hermes-agent-backup --skill songseegit clone --depth 1 https://github.com/john-data-chen/hermes-agent-backupWrote 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/john-data-chen/hermes-agent-backup/songsee)<a href="https://agentmods.dev/skills/john-data-chen/hermes-agent-backup/songsee"><img src="https://agentmods.dev/badge/skills/john-data-chen/hermes-agent-backup/songsee.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00324 |
| Sonnet 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 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.
This is a copy
100% identical to songsee — 0 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.
- 6d ago First seen · 84 lines · 20 tokens per session scan A d9d8d1394c63
songsee is a skill published in the GitHub repository john-data-chen/hermes-agent-backup (2 stars, last pushed 1mo ago), 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. It is 100% identical to songsee, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
papyrus-writing
LaTeX paper writing and editing. Load when working on a .tex document — writing or revising sections, fixing compilation errors, adding figures/tables/equations, or managing a bibliography. Also the co-author skill for the Papyrus app.
experiment_management
Set up and manage the experiment folder structure. This is Phase 0 — it runs before any analysis begins. All bookkeeping files are JSON (never markdown).
evaluate
Compare baseline and new implementation results. Produce the machine-readable final report result.json, update experiments.json, and append a row to comparison.json.
progress
Maintain a machine-readable progress file so dashboards, CLIs, and notebooks can poll the experiment's state at any time. The file is a JSON document — never markdown, never human-prose-first.
research
Read the materialized research source and extract actionable information needed to implement the proposed method. Record the findings as a structured JSON entry.
implement
Create a new Jupyter notebook implementing the method from the research paper, using the same data as the baseline. Record implementation details and measured metrics as a structured JSON entry.