claude-scientific-skills/docs/scientific-packages.md

Scientific Packages

Bioinformatics & Genomics

• AnnData - Annotated data matrices for single-cell genomics and h5ad files
• Arboreto - Gene regulatory network inference using GRNBoost2 and GENIE3
• BioPython - Sequence manipulation, NCBI database access, BLAST searches, alignments, and phylogenetics
• BioServices - Programmatic access to 40+ biological web services (KEGG, UniProt, ChEBI, ChEMBL)
• Cellxgene Census - Query and analyze large-scale single-cell RNA-seq data
• gget - Efficient genomic database queries (Ensembl, UniProt, NCBI, PDB, COSMIC)
• pysam - Read, write, and manipulate genomic data files (SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences) with pileup analysis, coverage calculations, and bioinformatics workflows
• PyDESeq2 - Differential gene expression analysis for bulk RNA-seq data
• Scanpy - Single-cell RNA-seq analysis with clustering, marker genes, and UMAP/t-SNE visualization
• scvi-tools - Probabilistic deep learning models for single-cell omics analysis. PyTorch-based framework providing variational autoencoders (VAEs) for dimensionality reduction, batch correction, differential expression, and data integration across modalities. Includes 25+ models: scVI/scANVI (RNA-seq integration and cell type annotation), totalVI (CITE-seq protein+RNA), MultiVI (multiome RNA+ATAC integration), PeakVI (ATAC-seq analysis), DestVI/Stereoscope/Tangram (spatial transcriptomics deconvolution), MethylVI (methylation), CytoVI (flow/mass cytometry), VeloVI (RNA velocity), contrastiveVI (perturbation studies), and Solo (doublet detection). Supports seamless integration with Scanpy/AnnData ecosystem, GPU acceleration, reference mapping (scArches), and probabilistic differential expression with uncertainty quantification

Cheminformatics & Drug Discovery

• Datamol - Molecular manipulation and featurization with enhanced RDKit workflows
• DeepChem - Molecular machine learning, graph neural networks, and MoleculeNet benchmarks
• DiffDock - Diffusion-based molecular docking for protein-ligand binding prediction
• MedChem - Medicinal chemistry analysis, ADMET prediction, and drug-likeness assessment
• Molfeat - 100+ molecular featurizers including fingerprints, descriptors, and pretrained models
• PyTDC - Therapeutics Data Commons for drug discovery datasets and benchmarks
• RDKit - Cheminformatics toolkit for molecular I/O, descriptors, fingerprints, and SMARTS
• TorchDrug - PyTorch-based machine learning platform for drug discovery with 40+ datasets, 20+ GNN models for molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis planning

Proteomics & Mass Spectrometry

• matchms - Processing and similarity matching of mass spectrometry data with 40+ filters, spectral library matching (Cosine, Modified Cosine, Neutral Losses), metadata harmonization, molecular fingerprint comparison, and support for multiple file formats (MGF, MSP, mzML, JSON)
• pyOpenMS - Comprehensive mass spectrometry data analysis for proteomics and metabolomics (LC-MS/MS processing, peptide identification, feature detection, quantification, chemical calculations, and integration with search engines like Comet, Mascot, MSGF+)

Medical Imaging & Digital Pathology

• histolab - Digital pathology toolkit for whole slide image (WSI) processing and analysis. Provides automated tissue detection, tile extraction for deep learning pipelines, and preprocessing for gigapixel histopathology images. Key features include: multi-format WSI support (SVS, TIFF, NDPI), three tile extraction strategies (RandomTiler for sampling, GridTiler for complete coverage, ScoreTiler for quality-driven selection), automated tissue masks with customizable filters, built-in scorers (NucleiScorer, CellularityScorer), pyramidal image handling, visualization tools (thumbnails, mask overlays, tile previews), and H&E stain decomposition. Supports multiple tissue sections, artifact removal, pen annotation exclusion, and reproducible extraction with seeding. Use cases: creating training datasets for computational pathology, extracting informative tiles for tumor classification, whole-slide tissue characterization, quality assessment of histology samples, automated nuclei density analysis, and preprocessing for digital pathology deep learning workflows
• PathML - Comprehensive computational pathology toolkit for whole slide image analysis, tissue segmentation, and machine learning on pathology data. Provides end-to-end workflows for digital pathology research including data loading, preprocessing, feature extraction, and model deployment
• 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
• scikit-learn - Machine learning algorithms, preprocessing, and model selection
• scikit-survival - Survival analysis and time-to-event modeling with censored data. Built on scikit-learn, provides Cox proportional hazards models (CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis with elastic net regularization), ensemble methods (Random Survival Forests, Gradient Boosting), Survival Support Vector Machines (linear and kernel), non-parametric estimators (Kaplan-Meier, Nelson-Aalen), competing risks analysis, and specialized evaluation metrics (concordance index, time-dependent AUC, Brier score). Handles right-censored data, integrates with scikit-learn pipelines, and supports feature selection and hyperparameter tuning via cross-validation
• SHAP - Model interpretability and explainability using Shapley values from game theory. Provides unified approach to explain any ML model with TreeExplainer (fast exact explanations for XGBoost/LightGBM/Random Forest), DeepExplainer (TensorFlow/PyTorch neural networks), KernelExplainer (model-agnostic), and LinearExplainer. Includes comprehensive visualizations (waterfall plots for individual predictions, beeswarm plots for global importance, scatter plots for feature relationships, bar/force/heatmap plots), supports model debugging, fairness analysis, feature engineering guidance, and production deployment
• statsmodels - Statistical modeling and econometrics (OLS, GLM, logit/probit, ARIMA, time series forecasting, hypothesis testing, diagnostics)
• Torch Geometric - Graph Neural Networks for molecular and geometric data
• Transformers - State-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks. Provides 1M+ pre-trained models accessible via pipelines (text-classification, NER, QA, summarization, translation, text-generation, image-classification, object-detection, ASR, VQA), comprehensive training via Trainer API with distributed training and mixed precision, flexible text generation with multiple decoding strategies (greedy, beam search, sampling), and Auto classes for automatic architecture selection (BERT, GPT, T5, ViT, BART, etc.)
• UMAP-learn - Dimensionality reduction and manifold learning

Materials Science & Chemistry

• Astropy - Astronomy and astrophysics (coordinates, cosmology, FITS files)
• COBRApy - Constraint-based metabolic modeling and flux balance analysis
• Pymatgen - Materials structure analysis, phase diagrams, and electronic structure

Data Analysis & Visualization

• Dask - Parallel computing for larger-than-memory datasets with distributed DataFrames, Arrays, Bags, and Futures
• Data Commons - Programmatic access to public statistical data from global sources including census bureaus, health organizations, and environmental agencies. Provides unified Python API for querying demographic data, economic indicators, health statistics, and environmental datasets through a knowledge graph interface. Features three main endpoints: Observation (statistical time-series queries for population, GDP, unemployment rates, disease prevalence), Node (knowledge graph exploration for entity relationships and hierarchies), and Resolve (entity identification from names, coordinates, or Wikidata IDs). Seamless Pandas integration for DataFrames, relation expressions for hierarchical queries, data source filtering for consistency, and support for custom Data Commons instances
• Matplotlib - Publication-quality plotting and visualization
• Polars - High-performance DataFrame operations with lazy evaluation
• Seaborn - Statistical data visualization with dataset-oriented interface, automatic confidence intervals, publication-quality themes, colorblind-safe palettes, and comprehensive support for exploratory analysis, distribution comparisons, correlation matrices, regression plots, and multi-panel figures
• SimPy - Process-based discrete-event simulation framework for modeling systems with processes, queues, and resource contention (manufacturing, service operations, network traffic, logistics). Supports generator-based process definition, multiple resource types (Resource, PriorityResource, PreemptiveResource, Container, Store), event-driven scheduling, process interaction mechanisms (signaling, interruption, parallel/sequential execution), real-time simulation synchronized with wall-clock time, and comprehensive monitoring capabilities for utilization, wait times, and queue statistics
• ReportLab - Programmatic PDF generation for reports and documents

Phylogenetics & Trees

• ETE Toolkit - Phylogenetic tree manipulation, visualization, and analysis

Genomics Tools

• deepTools - NGS data analysis (ChIP-seq, RNA-seq, ATAC-seq) with BAM/bigWig files
• FlowIO - Flow Cytometry Standard (FCS) file reading and manipulation
• scikit-bio - Bioinformatics sequence analysis and diversity metrics
• Zarr - Chunked, compressed N-dimensional array storage

Multi-omics & AI Agent Frameworks

• BIOMNI - Autonomous biomedical AI agent framework from Stanford SNAP lab for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Combines LLM reasoning with code execution and ~11GB of integrated biomedical databases (Ensembl, NCBI Gene, UniProt, PDB, AlphaFold, ClinVar, OMIM, HPO, PubMed, KEGG, Reactome, GO). Supports multiple LLM providers (Claude, GPT-4, Gemini, Groq, Bedrock). Includes A1 agent class for autonomous task decomposition, BiomniEval1 benchmark framework, and MCP server integration. Use cases: CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, protein structure analysis, literature synthesis, and multi-omics integration

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