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Distributed Dask Workflows

K-Dense-AI/claude-scientific-skills

Dask enables larger-than-memory pandas/NumPy workflows through parallel, distributed execution across dataframes, arrays, bags, and futures, making it ideal for scaling ETL, analytics, or custom computations across cores or clusters.

Compute Resource Monitor

get-available-resources

K-Dense-AI/claude-scientific-skills

Assesses available CPU, GPU, memory, and disk before computationally intensive scientific work, emitting a JSON summary with strategic recommendations on parallelism, memory handling, and GPU backends so you can choose the optimal compute pattern.

Molecular Featurization Hub

K-Dense-AI/claude-scientific-skills

Molfeat is a Python hub for turning SMILES or RDKit molecules into 100+ featurizers and pretrained embeddings, powering QSAR, screening, similarity, and deep learning workflows with transformers, calculators, caching, and parallel processing.

Polars DataFrame Engine

K-Dense-AI/claude-scientific-skills

Lightning-fast Arrow-based DataFrame library for Python and Rust that uses lazy evaluation and parallel execution to power 1-100GB ETL pipelines as a faster pandas replacement.

TileDB VCF Storage

K-Dense-AI/claude-scientific-skills

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

Zarr Python Arrays

K-Dense-AI/claude-scientific-skills

Handles chunked, compressed N-D arrays in Python for cloud-friendly workflows; integrates seamlessly with NumPy, Dask, and Xarray to enable parallel I/O, sharding, and persistence to S3/GCS for large-scale scientific pipelines.

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