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Polars DataFrame Analytics

K-Dense-AI/scientific-agent-skills

High-performance Python DataFrame library built on Apache Arrow. Covers expression-based API, lazy query optimization, streaming out-of-core processing, aggregations, window functions, joins, and pandas migration for ETL and analytics pipelines.

Polars Bio Genomic Interval Toolkit

K-Dense-AI/scientific-agent-skills

High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Supports overlap, nearest, merge, coverage, complement and subtract for BED/VCF/BAM/GFF files, with streaming and cloud-native access as a faster bioframe alternative.

PrimeKG Knowledge Graph Query

K-Dense-AI/scientific-agent-skills

Query the PrimeKG precision medicine knowledge graph to search genes, drugs, diseases, and phenotypes, retrieve associated neighbors, analyze disease context, and explore drug repurposing paths.

PyDESeq2 RNA-seq Analysis

K-Dense-AI/scientific-agent-skills

Perform differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Pymatgen Materials Analysis

K-Dense-AI/scientific-agent-skills

Analyze, validate, convert, and transform materials structures and computed data using pymatgen APIs, including symmetry, phase diagrams, and bounded Materials Project queries with provenance.

PyMC Bayesian Modeling

K-Dense-AI/scientific-agent-skills

Build, fit, and validate Bayesian models with PyMC 6, covering hierarchical models, MCMC/NUTS sampling, variational inference, prior and posterior predictive checks, diagnostics, and LOO/WAIC model comparison.

Pymoo Multi-Objective Optimization

K-Dense-AI/scientific-agent-skills

Python framework for single- and multi-objective optimization with NSGA-II/III, MOEA/D, and SPEA2. Supports constraints, ZDT/DTLZ benchmarks, Pareto front analysis, and visualization for engineering design.

PyOpenMS Mass Spectrometry

K-Dense-AI/scientific-agent-skills

Python bindings to OpenMS for computational mass spectrometry. Analyze proteomics and metabolomics LC-MS/MS data: feature detection, peptide and protein identification, label-free and isobaric quantification, adduct and accurate-mass annotation, plus ready-to-run workflow scripts.

Python Genomic File Processing

K-Dense-AI/scientific-agent-skills

A Python library for reading, querying, filtering, and writing genomic file formats including SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix data. Supports pileup, coverage analysis, indexing, and CRAM reference operations with HTSlib integration. Ideal for bioinformatics workflows requiring low-level streaming access to genomic data.

RDKit Cheminformatics Toolkit

K-Dense-AI/scientific-agent-skills

Python cheminformatics toolkit for fine-grained molecular control: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D coordinate generation, similarity, reactions, and visualization.

Academic Research Literature Lookup

research-lookup

K-Dense-AI/scientific-agent-skills

Compile verified scholarly evidence and references for scientific manuscripts or research briefs. Runs multi-pass Parallel Search with Extract source verification, ranked deduplication, and optional Research/Chat backends to deliver a manuscript-ready research packet targeting 60 unique references.

Scanpy Single-Cell Analysis Pipeline

K-Dense-AI/scientific-agent-skills

Scanpy is a scalable Python toolkit for single-cell RNA-seq analysis, covering quality control, normalization, dimensionality reduction, clustering, and visualization. It supports converting R formats to h5ad and includes ready-to-run CLI scripts for end-to-end workflows, making it ideal for exploratory scRNA-seq analysis and publication-quality plotting.

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