Explore skills

A Python library for working with geospatial vector data like shapefiles, GeoJSON, and GeoPackage files. It's used for spatial analysis, geometric operations, coordinate transformations, and other tasks involving vector geographic data, supporting PostGIS databases and interactive maps.

A low-level plotting library for full customization, providing fine-grained control over every plot element. It's ideal for creating novel plot types or integrating with specific scientific workflows, with export options to PNG/PDF/SVG for publication.

A comprehensive biosignal processing toolkit for analyzing physiological data such as ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill for processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements.

Perform and analyze molecular dynamics simulations using OpenMM and MDAnalysis, covering system setup, force field definition, energy minimization, production MD, and trajectory analysis (RMSD, RMSF, contact maps, free energy surfaces). Applicable to structural biology, drug binding, and biophysics.

Runs pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interprets the results. Use whenever the user has a set of genes and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched, covering over-representation analysis.

A complete mass spectrometry analysis platform for proteomics workflows, including feature detection, peptide identification, and protein quantification. It supports extensive file formats and algorithms, ideal for comprehensive MS data processing; for simple spectral comparison, use matchms.

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles.

Automated LLM-driven hypothesis generation and testing on tabular datasets. Use to systematically explore patterns in empirical data, combining literature insights with data-driven hypothesis testing.

PyTorch-native graph neural networks for molecules and proteins, suitable for custom GNN architectures in drug discovery, protein modeling, or knowledge graph reasoning. It's best for custom model development, protein property prediction, and retrosynthesis; for pre-trained models and diverse featurizers, use deepchem, and for benchmark datasets, use pytdc.

This skill is for working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use it for managing biological datasets, tracking workflows, curating data with ontologies, building lakehouses, or ensuring data lineage and reproducibility.

Facilitate a multi-perspective Mind Council deliberation for any question, decision, or creative challenge, providing diverse viewpoints and aiding in complex problem-solving.

A biological data toolkit for microbiome analysis, offering sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, and FASTA/Newick I/O.