Coding & Dev Tools

k-dense-ai Bundle

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

k-dense-ai Bundle

Design, simulate, and run quantum circuits on Google Quantum AI hardware and partner backends using Cirq.

k-dense-ai Bundle

Query 20+ bioinformatics databases from the command line or Python for gene information, sequences, protein structures, enrichment analysis, and more.

k-dense-ai Bundle

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.

k-dense-ai Bundle

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.

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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.

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

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.

k-dense-ai Bundle

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.

k-dense-ai Bundle

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.

k-dense-ai Bundle

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

k-dense-ai Bundle

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

k-dense-ai Bundle

Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.

k-dense-ai Bundle

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

k-dense-ai Bundle

Predict 3D binding poses of small molecule ligands to protein targets using diffusion-based molecular docking, supporting single complexes, batch processing, and virtual screening.

k-dense-ai Bundle

Run computational fluid dynamics simulations using Python, including Navier-Stokes equations, shallow water, and stratified flows with pseudospectral methods and HPC support.

k-dense-ai Bundle

Install, configure, and extend Pi, a terminal coding harness, with support for custom providers, models, extensions, skills, packages, themes, SDK integration, RPC mode, JSON event streams, and ecosystem packages for subagent delegation, MCP servers, interactive forms, and web access.

k-dense-ai Bundle

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

k-dense-ai Bundle

Analyze and manipulate crystal structures, compute phase diagrams, and access the Materials Project database using the pymatgen library.

k-dense-ai Bundle

Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.

k-dense-ai Bundle

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

k-dense-ai Bundle

Train reinforcement learning agents at millions of steps per second using optimized PPO, vectorized environments, and multi-agent support.

k-dense-ai Skill

Store, query, and export genomic variant data (VCF/BCF) using TileDB's sparse array technology for scalable population genomics workflows.

k-dense-ai Bundle

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

k-dense-ai Bundle

Manipulate phylogenetic trees, detect evolutionary events, integrate NCBI taxonomy, and create publication-quality visualizations using the ETE toolkit.

k-dense-ai Bundle

Analyze biological sequences, alignments, phylogenetic trees, and diversity metrics (alpha/beta, UniFrac) with ordination (PCoA) and PERMANOVA for microbiome and community ecology data.

k-dense-ai Bundle

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.