Skill UI

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

High-performance Rust toolkit with Python bindings for genomic interval analysis, covering overlap detection, coverage track generation, ML tokenization, reference sequence handling, fragment processing, and scoring workflows for sequencing datasets.

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

Manage microscopy data with the OMERO Python API: access projects/datasets/images, retrieve and analyze pixel data, handle ROIs and annotations, store results in tables, and automate batch workflows for high-content screening.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

Pydicom is a Python toolkit for reading, writing, and manipulating DICOM medical imaging files, helping teams extract pixel data, manage metadata, anonymize records, convert to standard formats, and integrate with PACS workflows in healthcare research and radiology applications.

PyOpenMS provides Python bindings for OpenMS to drive complete mass spectrometry workflows, covering data IO, spectral processing, feature/peptide-protein identification, quantitation, and metabolomics pipelines.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

A robust Python client wrapper for interacting with the Zotero Web API v3. This skill allows users to programmatically manage academic references, including retrieving, creating, updating, and deleting items, collections, tags, and attachments. Ideal for academic researchers building automated workflows, bulk exporting citations (e.g., BibTeX), or integrating Zotero data into larger research applications.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.