songsee
Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
Best use case
songsee is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
Teams using songsee should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/songsee/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How songsee Compares
| Feature / Agent | songsee | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# songsee Generate spectrograms and multi-panel audio feature visualizations from audio files. ## Prerequisites Requires [Go](https://go.dev/doc/install): ```bash go install github.com/steipete/songsee/cmd/songsee@latest ``` Optional: `ffmpeg` for formats beyond WAV/MP3. ## Quick Start ```bash # 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
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