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Use when auditing screenshots or live UI for cognitive load, usability friction, and heuristic violations.
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Basado en la clasificación ocupacional SOC
| name | ux-cognitive-auditor |
| description | Use when auditing screenshots or live UI for cognitive load, usability friction, and heuristic violations. |
This skill defines the methodology, heuristics evaluation criteria, and checklist for assessing the extraneous cognitive load and UX of the Audio Wandas Analyzer VS Code Extension using screenshots captured from a live VS Code Electron environment.
When performing a visual UX audit using screenshots, evaluate the interface according to two primary dimensions: Extraneous Cognitive Load and Nielsen/Krug Usability Heuristics.
Extraneous cognitive load is mental effort spent on processing information or interface mechanisms that do not directly help the user accomplish their task.
Each identified issue in UX_AUDIT_REPORT.md must be classified under:
UXH-NNN (e.g., UXH-001)P0: Critical blocker causing complete context loss, frozen UI, or critical user confusion. Must be fixed immediately.P1: Significant friction. Heavy cognitive load or poor heuristics violation, but the user can still bypass it with difficulty.P2: Minor improvement. Styling misalignment, minor discoverability issues, or lack of polished micro-animations.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.