Research

Pysam is a documentation skill for genomic data analysis using Python. It provides comprehensive reference guides for reading and writing sequencing alignment files (SAM/BAM/CRAM), genetic variant files (VCF/BCF), and sequence files (FASTA/FASTQ), along with practical code examples for bioinformatics workflows like coverage analysis and variant filtering.

PufferLib is a high-performance reinforcement learning framework optimized for fast parallel training. It provides templates for creating custom environments, training scripts with PPO, and seamless integration with popular RL frameworks like Gymnasium and PettingZoo, achieving millions of steps per second.

PyOpenMS is a comprehensive documentation skill for computational mass spectrometry using Python. It provides guidance for proteomics workflows including feature detection, peptide identification, protein quantification, and LC-MS/MS data processing pipelines.

This skill provides comprehensive guidance for using pymoo, a Python framework for multi-objective optimization. It covers evolutionary algorithms (NSGA-II, NSGA-III, MOEA/D), benchmark problems (ZDT, DTLZ), constraint handling, custom problem definition, and multi-criteria decision making for selecting preferred solutions from Pareto fronts.

A comprehensive Bayesian modeling skill built on PyMC 5.x that enables probabilistic programming with MCMC sampling (NUTS), variational inference, and hierarchical models. Includes diagnostic utilities for convergence checking, model comparison using LOO/WAIC, and templates for common model patterns like linear regression and multilevel models.

Pymatgen is a comprehensive Python library for materials analysis that enables working with crystal structures, computing phase diagrams, analyzing electronic structure data, and accessing the Materials Project database. It supports 100+ file formats and integrates with major computational chemistry codes like VASP, Gaussian, and Quantum ESPRESSO.

PyLabRobot is a vendor-agnostic laboratory automation skill that helps you control liquid handling robots (Hamilton, Opentrons, Tecan), plate readers, pumps, heater shakers, and other lab equipment through a unified Python interface. It includes simulation capabilities for testing protocols without physical hardware.

PyDESeq2 is a bioinformatics skill for analyzing bulk RNA-seq count data to identify differentially expressed genes. It provides a complete workflow from data loading through statistical testing (Wald tests with FDR correction), including support for single-factor and multi-factor experimental designs, optional LFC shrinkage, and visualization with volcano/MA plots.

This skill enables AI-powered web searches using Perplexity models via OpenRouter, providing real-time answers with source citations. It's ideal for finding current information, scientific literature, and facts beyond Claude's training data cutoff, supporting multiple model tiers from cost-effective basic searches to advanced multi-step reasoning.

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.

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 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.

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.

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 should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.

Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.