Scientific Computing

This skill provides comprehensive reference documentation for MATLAB and GNU Octave numerical computing environments. It covers matrix operations, linear algebra, data visualization, file I/O, differential equations, signal processing, and Python integration with extensive code examples and best practices.

This skill provides comprehensive documentation and Python templates for programming Opentrons lab automation robots (OT-2 and Flex). It covers liquid handling operations, hardware module control (thermocycler, heater-shaker, magnetic modules), and common protocol patterns like serial dilutions and PCR setup.

This skill provides comprehensive documentation for integrating with OMERO, an open-source platform for managing microscopy images. It covers connection management, data access, image processing, ROI creation, metadata annotations, OMERO tables, and server-side scripts for batch operations in scientific imaging workflows.

This skill provides comprehensive documentation for integrating with the LatchBio platform for bioinformatics workflows. It covers workflow creation using Python decorators, cloud data management with LatchFile/LatchDir, resource configuration for CPU/GPU tasks, and pre-built verified workflows like AlphaFold and DESeq2. No code is executed - it serves as a reference guide.

This skill provides programmatic access to LabArchives electronic lab notebook platform. It enables users to authenticate, list and backup notebooks, create entries, upload attachments, and integrate with third-party scientific tools like Protocols.io, Jupyter, and REDCap for research documentation workflows.

Provides comprehensive API access to the ZINC database, a UCSF-maintained repository of 230M+ purchasable chemical compounds. Supports searching by ZINC ID or SMILES notation, similarity searches, random sampling, and downloading 3D structures for molecular docking studies in drug discovery workflows.

This skill provides access to the BRENDA enzyme database, the world's most comprehensive enzyme information system. It enables researchers to retrieve kinetic parameters (Km, kcat, Vmax), reaction equations, substrate specificities, and optimal conditions for over 45,000 enzymes. The skill also supports metabolic pathway construction and retrosynthetic analysis for enzyme engineering applications.

This skill enables access to the AlphaFold Protein Structure Database, providing code examples and documentation for retrieving AI-predicted 3D protein structures. It supports querying by UniProt ID, downloading coordinate files (PDB/mmCIF), analyzing confidence metrics (pLDDT, PAE), and bulk data access via Google Cloud for structural biology and drug discovery workflows.

A documentation skill that teaches how to use Zarr, a Python library for storing large N-dimensional arrays with chunking and compression. It covers array creation, storage backends (local, cloud, memory), compression codecs, parallel computing with Dask, and integration with NumPy and Xarray for scientific computing workflows.

A comprehensive reference skill for scikit-bio, a Python bioinformatics library. Provides documentation and code examples for biological sequence manipulation, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination analysis (PCoA, CCA), statistical tests (PERMANOVA, ANOSIM), and microbiome data processing with FASTA/BIOM file format support.

A comprehensive reference skill for QuTiP (Quantum Toolbox in Python), providing documentation for simulating open quantum systems, master equations, and quantum optics. Includes detailed guides for time evolution solvers, visualization tools, and advanced features like Floquet theory and HEOM methods.

This skill provides extensive guidance for working with IBM's Qiskit quantum computing framework. It covers building quantum circuits, running simulations, executing on real IBM Quantum hardware, and implementing quantum algorithms like VQE, QAOA, and Grover's search. The skill includes reference documentation for setup, primitives, transpilation, visualization, and various quantum applications in chemistry, optimization, and machine learning.

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.

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.

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.

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.

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.

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.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

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. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.