Move Neuropixels-Analysis to new Neuroscience category

Create new "🧠 Neuroscience & Electrophysiology" category for better
classification. Neuropixels is research neuroscience (animal models,
invasive recordings) rather than clinical healthcare AI.

- Remove from Healthcare AI section
- Add new Neuroscience & Electrophysiology section
- Keep NeuroKit2 in Healthcare AI (surface biosignals, clinical focus)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

docs/scientific-skills.md

@@ -95,6 +95,8 @@
 ### Healthcare AI & Clinical Machine Learning
 - **NeuroKit2** - Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Key features include: automated signal processing pipelines (cleaning, peak detection, delineation, quality assessment), heart rate variability analysis across time/frequency/nonlinear domains (SDNN, RMSSD, LF/HF, DFA, entropy measures), EEG analysis (frequency band power, microstates, source localization), autonomic nervous system assessment (sympathetic indices, respiratory sinus arrhythmia), comprehensive complexity measures (25+ entropy types, 15+ fractal dimensions, Lyapunov exponents), event-related and interval-related analysis modes, epoch creation and averaging for stimulus-locked responses, multi-signal integration with unified workflows, and extensive signal processing utilities (filtering, decomposition, peak correction, spectral analysis). Includes modular reference documentation across 12 specialized domains. Use cases: heart rate variability for cardiovascular health assessment, EEG microstates for consciousness studies, electrodermal activity for emotion research, respiratory variability analysis, psychophysiology experiments, affective computing, stress monitoring, sleep staging, autonomic dysfunction assessment, biofeedback applications, and multi-modal physiological signal integration for comprehensive human state monitoring
 - **PyHealth** - Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. Provides specialized tools for electronic health records (EHR), physiological signals, medical imaging, and clinical text analysis. Key features include: 10+ healthcare datasets (MIMIC-III/IV, eICU, OMOP, sleep EEG, COVID-19 CXR), 20+ predefined clinical prediction tasks (mortality, hospital readmission, length of stay, drug recommendation, sleep staging, EEG analysis), 33+ models (Logistic Regression, MLP, CNN, RNN, Transformer, GNN, plus healthcare-specific models like RETAIN, SafeDrug, GAMENet, StageNet), comprehensive data processing (sequence processors, signal processors, medical code translation between ICD-9/10, NDC, RxNorm, ATC systems), training/evaluation utilities (Trainer class, fairness metrics, calibration, uncertainty quantification), and interpretability tools (attention visualization, SHAP, ChEFER). 3x faster than pandas for healthcare data processing. Use cases: ICU mortality prediction, hospital readmission risk assessment, safe medication recommendation with drug-drug interaction constraints, sleep disorder diagnosis from EEG signals, medical code standardization and translation, clinical text to ICD coding, length of stay estimation, and any clinical ML application requiring interpretability, fairness assessment, and calibrated predictions for healthcare deployment
+
+### Neuroscience & Electrophysiology
 - **Neuropixels-Analysis** - Comprehensive toolkit for analyzing Neuropixels high-density neural recordings using SpikeInterface, Allen Institute, and International Brain Laboratory (IBL) best practices. Supports the full workflow from raw data to publication-ready curated units. Key features include: data loading from SpikeGLX, Open Ephys, and NWB formats, preprocessing pipelines (highpass filtering, phase shift correction for Neuropixels 1.0, bad channel detection, common average referencing), motion/drift estimation and correction (kilosort_like and nonrigid_accurate presets), spike sorting integration (Kilosort4 GPU, SpykingCircus2, Mountainsort5 CPU), comprehensive postprocessing (waveform extraction, template computation, spike amplitudes, correlograms, unit locations), quality metrics computation (SNR, ISI violations, presence ratio, amplitude cutoff, drift metrics), automated curation using Allen Institute and IBL criteria with configurable thresholds, AI-assisted visual curation for uncertain units using Claude API, and export to Phy for manual review or NWB for sharing. Supports Neuropixels 1.0 (960 electrodes, 384 channels) and Neuropixels 2.0 (single and 4-shank configurations). Use cases: extracellular electrophysiology analysis, spike sorting from silicon probes, neural population recordings, systems neuroscience research, unit quality assessment, publication-ready neural data processing, and integration of AI-assisted curation for borderline units
 
 ### Protein Engineering & Design