This skill provides comprehensive documentation and code examples for PennyLane, a quantum computing library for training quantum circuits like neural networks. It covers quantum machine learning, chemistry simulations (VQE), optimization algorithms (QAOA), and integration with classical ML frameworks like PyTorch, JAX, and TensorFlow.

PathML is a full-featured computational pathology toolkit for analyzing whole-slide pathology images. It supports 160+ slide formats, provides preprocessing pipelines for H&E stain normalization and tissue detection, includes pre-trained models for nucleus segmentation, enables graph-based spatial analysis, and supports multiparametric imaging platforms like CODEX and Vectra for spatial proteomics workflows.

Molfeat is a comprehensive guide for molecular featurization in machine learning. It provides documentation for converting chemical structures (SMILES strings) into numerical representations using 100+ featurizers including fingerprints (ECFP, MACCS), descriptors (RDKit, Mordred), and pretrained models (ChemBERTa, GIN). Ideal for QSAR modeling, virtual screening, and cheminformatics tasks.

Modal is a documentation skill that guides users in running Python code on Modal's serverless cloud platform. It covers GPU-accelerated computing, autoscaling, persistent storage with Volumes, scheduled jobs, and web endpoints for ML workloads and batch processing.

This skill helps chemists and drug discovery researchers filter and prioritize compound libraries using established medicinal chemistry rules like Lipinski's Rule of Five, PAINS filters, and structural alerts. It can process molecules in batch with parallel processing and generate detailed filtering reports.

This skill provides expert guidance on using matplotlib for data visualization in Python. It covers both pyplot and object-oriented interfaces, includes comprehensive reference documentation for plot types, styling, and troubleshooting, plus ready-to-use template scripts for creating publication-quality figures.

This skill provides comprehensive guidance for using the matchms Python library for mass spectrometry data processing and analysis. It covers spectral similarity calculations, compound identification from spectral libraries, data filtering, and format conversion for metabolomics research workflows.

MarkItDown converts files and office documents to Markdown format. It supports PDF, Word, PowerPoint, Excel, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, and EPubs. Optional AI integration via OpenRouter provides enhanced image descriptions for visual content.

Hypogenic is a framework for automated scientific hypothesis generation using large language models. It can generate testable hypotheses from data alone (HypoGeniC), combine literature insights with empirical patterns (HypoRefine), or use both approaches together (Union methods). The skill provides configuration templates and documentation for accelerating research discovery across domains like deception detection, content analysis, and predictive modeling.

Histolab is a documentation skill for the histolab Python library used in digital pathology. It provides comprehensive guidance on processing whole slide images (WSI), including tissue detection, tile extraction strategies, preprocessing filters, and visualization techniques for preparing datasets for deep learning pipelines.

gget is a bioinformatics tool that provides quick access to over 20 genomic databases through both command-line and Python interfaces. It enables gene searches, BLAST/BLAT sequence analysis, AlphaFold structure predictions, single-cell expression queries, enrichment analysis, and disease/drug associations - all through a unified, consistent interface.

FlowIO is a documentation skill that teaches Claude how to help users work with Flow Cytometry Standard (FCS) files. It provides guidance on parsing FCS metadata, extracting event data as NumPy arrays, creating new FCS files, and handling multi-dataset files for scientific flow cytometry data processing.

A comprehensive phylogenetic tree analysis toolkit built on ETE (Environment for Tree Exploration). It enables tree manipulation (loading, pruning, rooting), evolutionary analysis (detecting duplications and speciations, ortholog/paralog identification), NCBI taxonomy integration, and publication-quality visualization in PDF/SVG/PNG formats. Ideal for phylogenomics research and clustering analysis.

DiffDock is a molecular docking skill for computational drug discovery. It helps predict how small molecule ligands bind to protein targets using diffusion-based deep learning, providing binding pose predictions and confidence scores for structure-based drug design workflows.

deepTools is a bioinformatics skill for analyzing next-generation sequencing (NGS) data. It helps with quality control, normalization, and visualization of ChIP-seq, RNA-seq, and ATAC-seq experiments, providing workflow generators and comprehensive documentation for common analyses.

A comprehensive molecular machine learning skill using DeepChem for predicting chemical properties like solubility and toxicity. It supports graph neural networks, transfer learning with pretrained models (ChemBERTa, GROVER), and MoleculeNet benchmarks for drug discovery and materials science applications.

A comprehensive documentation skill for Dask, a Python library for parallel and distributed computing. It provides detailed reference guides for working with larger-than-memory datasets using DataFrames, Arrays, Bags, and Futures, along with scheduler selection and best practices for performance optimization.

COBRApy is a documentation skill for systems biology and metabolic engineering analysis. It provides comprehensive guidance on using the COBRApy Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, including flux balance analysis, gene knockouts, flux sampling, and SBML model handling.

This skill provides comprehensive documentation and code examples for Google's Cirq quantum computing framework. It covers quantum circuit design, simulation, noise modeling, hardware integration with multiple providers (Google, IonQ, Azure, AQT, Pasqal), and experiment design patterns like VQE and QAOA.

This skill provides comprehensive guidance for programmatically accessing the CZ CELLxGENE Census, a collection of 61+ million single-cell genomics data. It covers querying expression data by cell type, tissue, or disease, integrating with PyTorch for machine learning, and using scanpy for analysis workflows.

A comprehensive bioinformatics skill that provides a unified Python interface to 40+ biological databases including UniProt, KEGG, ChEMBL, and Reactome. It enables protein analysis, pathway discovery, compound searches, sequence similarity searches, and cross-database identifier mapping for scientific research workflows.

A comprehensive reference skill for the Biopython library, providing documentation and code examples for computational molecular biology tasks. Covers sequence manipulation, NCBI database access (GenBank, PubMed), BLAST searches, protein structure analysis, phylogenetics, and advanced features like motif analysis and restriction enzyme mapping.

This skill provides comprehensive documentation and reference material for working with AnnData, a Python package for handling annotated data matrices used in single-cell genomics. It covers creating, reading, writing, and manipulating AnnData objects, along with best practices for memory management and integration with the scverse ecosystem (Scanpy, Muon, PyTorch).

This skill creates personalized coding tutorials tailored to your learning journey. It maintains a central library of tutorials at ~/coding-tutor-tutorials/, uses real code examples from your projects, tracks your understanding through quizzes, and implements spaced repetition to help concepts stick over time.

This skill provides a complete workflow for creating Claude Code skills. It includes scripts to initialize new skill directories with proper templates, validate skill structure and metadata, and package skills into distributable zip files. The skill also contains comprehensive documentation on skill anatomy, progressive disclosure patterns, and best practices for writing effective skills.

Upload, sync, and manage files across cloud storage providers like AWS S3, Cloudflare R2, Google Drive, and Dropbox using rclone. The skill verifies rclone installation, helps configure remotes, and provides commands for file transfers with progress tracking and verification.

This skill provides a unified interface for managing Git worktrees, enabling isolated parallel development. It handles creating worktrees from branches, listing active worktrees, switching between them, and cleaning up completed work, with automatic .env file copying to ensure environment consistency across worktrees.

This skill enables AI-powered image generation and editing using Google's Gemini API. It supports creating images from text prompts, editing existing images with natural language instructions, composing multiple images together, and iteratively refining results through multi-turn conversations.

This skill provides comprehensive design guidelines for creating distinctive, production-grade frontend interfaces. It covers typography choices, color themes, motion/animations, spatial composition, and background effects while explicitly avoiding generic 'AI slop' aesthetics like overused fonts and clichéd color schemes.

This skill provides a structured file-based todo tracking system for managing code review feedback, technical debt, and feature requests. Each todo is a markdown file with YAML frontmatter stored in a todos/ directory, supporting workflows for creation, triage, dependency tracking, and completion.