No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
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🔒 Is songsee safe to install? View the security audit →
🌊 songsee — FFT so pretty, your ears will be jealous. Features 9 visualization modes: spectrogram, mel, chroma, hpss, selfsim, loudness, tempogram, mfcc, flux 6 color palettes: classic, magma, inferno, viridis, gray, claw Auto-contrast: per-panel percentile normalization for readable heatmaps Combine modes: stack multiple visualizations in one grid image Universal input: WAV, MP3, or anything ffmpeg can handle Fast: native Go, no Python dependencies Flexible output: PNG or JPEG, customizable dimensions Install Docker: The image includes , so batch and server runs do not depend on host audio tooling. Quick Start Visualization Modes Perceptual frequency
| Stars | 55 |
| Forks | 3 |
| Language | Go |
| Category | AI Tool |
| License | MIT |
| Quality Score | 70.2578815380107/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-04 |
| Created | 2026-01-02 |
| Platforms | go |
| Est. Tokens | ~181k |
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songsee is 🌊 FFT so pretty, your ears will be jealous. See your sound—spectrograms, mel, chroma, and more.. It is categorized as a AI Tool with 55 GitHub stars.
songsee is primarily written in Go. It covers topics such as agents, music, visualization.
You can find installation instructions and usage details in the songsee GitHub repository at github.com/steipete/songsee. The project has 55 stars and 3 forks, indicating an active community.
songsee is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to songsee on Agent Skills Hub include melrose, arbor, review-analyzer-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: