All Skills

Explain machine learning model predictions using SHAP values, compute feature importance, and generate visualizations including waterfall, beeswarm, bar, scatter, force, and heatmap plots.

Process and analyze large tabular datasets (billions of rows) that exceed available RAM using lazy, out-of-core DataFrames with fast aggregations, visualization, and machine learning integration.

Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm) with formulas, formatting, financial models, and multi-sheet workbooks.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Deploy and serve AI/ML models on Modal's serverless cloud platform with on-demand GPUs, autoscaling containers, persistent storage, and scheduled jobs.

Solve single and multi-objective optimization problems using NSGA-II/III, MOEA/D, and other evolutionary algorithms with customizable operators, constraint handling, and benchmark problems.

Read, write, and manipulate genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

Simulate and analyze open quantum systems using QuTiP, including master equations, Lindblad dynamics, decoherence, and quantum optics.

Perform cheminformatics tasks including molecular I/O, descriptor calculation, fingerprinting, substructure search, and similarity analysis using the RDKit library.

Run cloud-native molecular modeling and drug-design workflows including pKa prediction, docking, molecular dynamics, and protein-ligand cofolding via a Python API.

Build discrete-event simulations of systems with processes, queues, resources, and time-based events using SimPy in Python.

Perform exact symbolic mathematics in Python — algebra, calculus, equation solving, symbolic linear algebra, and code generation via lambdify or LaTeX.

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores, drug sensitivity data, and gene effect profiles to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets.

Parse FCS (Flow Cytometry Standard) files v2.0-3.1, extract events as NumPy arrays, read metadata and channels, and convert to CSV or DataFrame for flow cytometry data preprocessing.

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Perform numerical computing, matrix operations, data analysis, and scientific visualization using MATLAB or GNU Octave.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.

Build and execute quantum circuits on IBM Quantum hardware, simulators, and third-party providers using the Qiskit framework.

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.

Submit protein sequences to the Adaptyv Bio Foundry for experimental characterization (binding, thermostability, expression, fluorescence) and retrieve results via API or Python SDK.

Create, read, manipulate, and store annotated data matrices using the AnnData Python package, designed for single-cell genomics and general-purpose annotated data workflows.

Perform astronomical data analysis with Astropy: coordinate transformations, unit conversions, FITS I/O, cosmological calculations, time handling, table operations, and WCS transformations.

Perform constraint-based metabolic modeling with COBRApy: run FBA, FVA, gene knockouts, flux sampling, and manage SBML models for systems biology and metabolic engineering.

Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.

Manage biological datasets and models with LaminDB, an open-source lineage-native lakehouse. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation, collections, branches, storage, and workflow integrations.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Apply medicinal chemistry filters for compound triage: drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and a custom query language for library filtering.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.

Query the 1000 Genomes Project dataset at the individual participant level to find variants, carriers, and relatedness information.

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.

Read, write, and modify DICOM medical imaging files, including pixel data extraction, metadata manipulation, anonymization, and format conversion.

Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.