Render audio as visual analytics with a small NumPy/SciPy pipeline: waveform, log-mel spectrogram, and spectral centroid over time, saved as PNG. Use when a use
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Works with the AI tools you already use
See it in action
You say
Analyze this recording for any silent gaps and show me the spectrogram.
Your agent does
I have generated a waveform and spectrogram for your audio. The analysis shows a 1.5s silent gap starting at the 4-second mark.
MEDIA:/tmp/audio_analysis.png
What you get
About this skill
The problem
Debugging audio issues like silent gaps, clipping, or frequency interference is impossible by looking at raw binary data or basic logs. Developers need a way to visually inspect audio content during automated processing or debugging sessions without opening a GUI.
What it does
- Generates time-domain waveforms to identify amplitude peaks and silence.
- Produces log-mel spectrograms to visualize frequency content over time.
- Converts compressed audio formats to mono WAV for consistent analysis.
- Calculates RMS and peak signal levels to compare multiple audio takes.
- Outputs high-resolution PNGs suitable for documentation or inline review.
Frameworks & tools
Python, NumPy, SciPy, Matplotlib, and FFmpeg.
Why this beats prompting it yourself
Standard LLMs cannot "see" audio files directly. This skill provides the specific signal processing pipeline needed to translate raw audio into a visual format the LLM can interpret and describe to you, ensuring math-heavy tasks like STFT windowing are handled correctly.
Use cases
- Identify silent segments or audio dropouts in long recordings.
- Verify the frequency output of synthesized sounds or signal generators.
- Compare two audio versions to detect clipping or noise floor differences.
- Generate visual thumbnails for audio asset management systems.
Known limitations
Requires FFmpeg installed on the system path. Not intended for real-time metering or high-precision music transcription.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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