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audio-wandas-analyzer
audio-wandas-analyzer contiene 9 skills recopiladas de kasahart, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Use when auditing screenshots or live UI for cognitive load, usability friction, and heuristic violations.
Use when reproducing or preventing runtime-only Webview UI bugs with real Chromium checks, Playwright smoke specs, static HTML-pattern linting, or dogfooding the UI smoke layers against a suspected regression.
Use when analyzing audio or vibration signals end-to-end, generating Jupyter Notebook analysis reports, comparing multiple measurement conditions, evaluating noise or sound quality, detecting anomalies in sensor data, or performing adaptive signal investigation driven by hypothesis and findings with wandas.
Use when starting with wandas, loading audio or sensor data from WAV or CSV files, creating signals from NumPy arrays, understanding ChannelFrame and other frame types, inspecting signal metadata, or setting up physical units (Pa, m/s²) for dB calculations.
Use when applying filters (lowpass, highpass, bandpass, A-weighting), normalizing signals, resampling, trimming, adding fades, computing RMS trends, calculating sound level (dB, A-weighting), or computing psychoacoustic metrics (loudness, roughness, sharpness) with wandas.
Use when performing FFT, STFT, Welch PSD estimation, 1/N octave band analysis, coherence, cross-spectral density, or transfer function analysis with wandas.
Use when plotting waveforms, frequency spectra, spectrograms, octave band charts, roughness heatmaps, overlaying multiple signals on the same axes, or configuring describe() with frequency range and colormap settings with wandas.
Use when reproducing or preventing runtime-only Webview UI bugs with real Chromium checks, Playwright smoke specs, static HTML-pattern linting, or dogfooding the UI smoke layers against a suspected regression.
Use when analyzing audio or vibration signals end-to-end, generating Jupyter Notebook analysis reports, comparing multiple measurement conditions, evaluating noise or sound quality, detecting anomalies in sensor data, or performing adaptive signal investigation driven by hypothesis and findings with wandas.