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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.
Passed 12 of 16 test cases with the skill, and 2 of 16 without it.
and 16 more in the full report
14 fixed2 passed either way5 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
I'd like a classic color palette spectrogram for `guitar_riff.wav` saved to `classic_spec.png`. What is the exact CLI command?
| Checked | Without skill | With skill |
|---|---|---|
| Uses songsee command line tool | Missed | Passed |
| Includes --style classic flag | Missed | Passed |
The instructions your model reads when the skill loads.
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal => bashweb_extract => web_fetch, plus web_search when discovery is neededsearch_files => grep_search and glob_searchbrowser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitlyWhen the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Generate spectrograms and multi-panel audio feature visualizations from audio files.
Requires Go:
go install github.com/steipete/songsee/cmd/songsee@latest
Optional: ffmpeg for formats beyond WAV/MP3.
# 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
test cases passed, vs 2 / 16 without
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