Added support for NeuroKit2

docs/scientific-packages.md

@@ -32,12 +32,14 @@
 - **pydicom** - Pure Python package for working with DICOM (Digital Imaging and Communications in Medicine) files. Provides comprehensive support for reading, writing, and manipulating medical imaging data from CT, MRI, X-ray, ultrasound, PET scans and other modalities. Key features include: pixel data extraction and manipulation with automatic decompression (JPEG/JPEG 2000/RLE), metadata access and modification with 1000+ standardized DICOM tags, image format conversion (PNG/JPEG/TIFF), anonymization tools for removing Protected Health Information (PHI), windowing and display transformations (VOI LUT application), multi-frame and 3D volume processing, DICOM sequence handling, and support for multiple transfer syntaxes. Use cases: medical image analysis, PACS system integration, radiology workflows, research data processing, DICOM anonymization, format conversion, image preprocessing for machine learning, multi-slice volume reconstruction, and clinical imaging pipelines
 
 ## 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
 
 ## Protein Engineering & Design
 - **ESM (Evolutionary Scale Modeling)** - State-of-the-art protein language models from EvolutionaryScale for protein design, structure prediction, and representation learning. Includes ESM3 (1.4B-98B parameter multimodal generative models for simultaneous reasoning across sequence, structure, and function with chain-of-thought generation, inverse folding, and function-conditioned design) and ESM C (300M-6B parameter efficient embedding models 3x faster than ESM2 for similarity analysis, classification, and feature extraction). Supports local inference with open weights and cloud-based Forge API for scalable batch processing. Use cases: novel protein design, structure prediction from sequence, sequence design from structure, protein embeddings, function annotation, variant generation, and directed evolution workflows
 
 ## Machine Learning & Deep Learning
+- **aeon** - Time series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing time series analysis, building predictive models on sequential data, or implementing workflows that involve distance metrics (DTW), transformations (ROCKET, Catch22), or deep learning for time series. Applicable for tasks like ECG classification, stock price forecasting, sensor anomaly detection, or activity recognition from wearable devices
 - **PyMC** - Bayesian statistical modeling and probabilistic programming
 - **PyMOO** - Multi-objective optimization with evolutionary algorithms
 - **PyTorch Lightning** - Deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automates training workflows (40+ tasks including epoch/batch iteration, optimizer steps, gradient management, checkpointing), supports multi-GPU/TPU training with DDP/FSDP/DeepSpeed strategies, includes LightningModule for model organization, Trainer for automation, LightningDataModule for data pipelines, callbacks for extensibility, and integrations with TensorBoard, Wandb, MLflow for experiment tracking
@@ -78,6 +80,9 @@
 ## Scientific Communication & Publishing
 - **Paper-2-Web** - Autonomous pipeline for transforming academic papers into multiple promotional formats using the Paper2All system. Converts LaTeX or PDF papers into: (1) Paper2Web - interactive, layout-aware academic homepages with responsive design, interactive figures, and mobile support; (2) Paper2Video - professional presentation videos with slides, narration, cursor movements, and optional talking-head generation using Hallo2; (3) Paper2Poster - print-ready conference posters with custom dimensions, professional layouts, and institution branding. Supports GPT-4/GPT-4.1 models, batch processing, QR code generation, multi-language content, and quality assessment metrics. Use cases: conference materials, video abstracts, preprint enhancement, research promotion, poster sessions, and academic website creation
 
+## Laboratory Automation & Equipment Control
+- **PyLabRobot** - Hardware-agnostic, pure Python SDK for automated and autonomous laboratories. Provides unified interface for controlling liquid handling robots (Hamilton STAR/STARlet, Opentrons OT-2, Tecan EVO), plate readers (BMG CLARIOstar), heater shakers, incubators, centrifuges, pumps, and scales. Key features include: modular resource management system for plates, tips, and containers with hierarchical deck layouts and JSON serialization; comprehensive liquid handling operations (aspirate, dispense, transfer, serial dilutions, plate replication) with automatic tip and volume tracking; backend abstraction enabling hardware-agnostic protocols that work across different robots; ChatterboxBackend for protocol simulation and testing without hardware; browser-based visualizer for real-time 3D deck state visualization; cross-platform support (Windows, macOS, Linux, Raspberry Pi); and integration capabilities for multi-device workflows combining liquid handlers, analytical equipment, and material handling devices. Use cases: automated sample preparation, high-throughput screening, serial dilution protocols, plate reading workflows, laboratory protocol development and validation, robotic liquid handling automation, and reproducible laboratory automation with state tracking and persistence
+
 ## Tool Discovery & Research Platforms
 - **ToolUniverse** - Unified ecosystem providing standardized access to 600+ scientific tools, models, datasets, and APIs across bioinformatics, cheminformatics, genomics, structural biology, and proteomics. Enables AI agents to function as research scientists through: (1) Tool Discovery - natural language, semantic, and keyword-based search for finding relevant scientific tools (Tool_Finder, Tool_Finder_LLM, Tool_Finder_Keyword); (2) Tool Execution - standardized AI-Tool Interaction Protocol for running tools with consistent interfaces; (3) Tool Composition - sequential and parallel workflow chaining for multi-step research pipelines; (4) Model Context Protocol (MCP) integration for Claude Desktop/Code. Supports drug discovery workflows (disease→targets→structures→screening→candidates), genomics analysis (expression→differential analysis→pathways), clinical genomics (variants→annotation→pathogenicity→disease associations), and cross-domain research. Use cases: accessing scientific databases (OpenTargets, PubChem, UniProt, PDB, ChEMBL, KEGG), protein structure prediction (AlphaFold), molecular docking, pathway enrichment, variant annotation, literature searches, and automated scientific workflows