Skill UI

This skill provides direct command-line access to the Paperzilla platform, allowing users to manage, summarize, and retrieve academic research data. Users can fetch canonical papers in markdown format, get the latest project recommendations, analyze paper relevance, export data (JSON/Atom), and provide feedback on recommendations. It is an essential tool for researchers managing a large volume of academic literature.

Biopython is a robust, comprehensive toolkit for computational molecular biology written in Python. It enables users to manipulate biological sequences (DNA, RNA, protein), parse standard formats (FASTA, GenBank, PDB, etc.), and programmatically access major bioinformatics databases like NCBI via Entrez. This skill is ideal for developing complex bioinformatics pipelines, including sequence alignment, structural analysis, and phylogenetic tree construction.

Provides programmatic access to massive, versioned public single-cell and spatial transcriptomics datasets from CZ CELLxGENE. This skill allows users to efficiently query specific subsets of cell metadata, gene expression matrices, embeddings, and summary statistics across various organisms, tissues, and diseases. It is ideal for large-scale, memory-efficient biological research and model training without full dataset downloads.

Datamol provides a simplified, Pythonic abstraction layer over RDKit, making complex molecular operations accessible for chemical data science. It supports core functionalities including SMILES parsing, structure standardization, calculation of molecular descriptors (e.g., LogP, MW), 3D conformer generation, and efficient parallel batch processing. It is ideal for streamlining molecular data handling in drug discovery and chemical research pipelines.

This skill provides programmatic access to the Cancer Dependency Map (DepMap), a comprehensive resource characterizing genetic dependencies in cancer cell lines. Use it to identify genes essential for specific cancer types, discover synthetic lethal interactions, and validate potential oncology drug targets using CRISPR and RNAi data.

gget is a comprehensive command-line tool and Python package offering unified access to over 20 genomic databases. It facilitates quick lookups for gene information, sequence analysis (BLAST), protein structures, and expression data, supporting both interactive CLI use and advanced programmatic Python workflows for biological research.

A comprehensive toolkit for securely inspecting and automating complex microscopy data workflows against OMERO.server. It provides deep programmatic access using omero-py, covering metadata, annotations, ROIs, rendering, and detailed data export/import planning. Essential for building controlled, large-scale data inventory and analysis pipelines.

Open Notebook is a self-hosted, open-source alternative to major AI research tools. It allows users to ingest diverse content—including PDFs, videos, and web pages—and organize them into private notebooks. Key features include context-aware AI chat, automated summarization, multi-speaker podcast generation, and full programmatic access via a comprehensive REST API. By running on your own infrastructure, it ensures complete data privacy and supports 16+ AI providers.

This skill provides programmatic access to the PrimeKG, a comprehensive knowledge graph integrating over 20 primary databases. It facilitates multiscale biological research by mapping complex relationships between genes, drugs, diseases, and phenotypes. Use it for drug repurposing, identifying novel disease targets, and analyzing molecular mechanisms across various biological scales.

A powerful Python library providing low-level, streaming access to standard genomics file formats supported by HTSlib (SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, etc.). It facilitates reading, writing, querying, and filtering genomic data, making it essential for bioinformatics workflows.

PyTDC provides a comprehensive toolkit for computational drug discovery and biomedical research. It facilitates the discovery of therapeutic machine learning tasks, accessing approved datasets, and managing complex benchmark groups. Users can apply task-appropriate data splits and evaluate predictions using standardized metrics, ensuring reproducibility in high-stakes scientific workflows. Emphasizes metadata-only discovery before any resource download.

A comprehensive guide and tooling mechanism for creating and auditing scientifically truthful, accessible, and publication-ready figures. It enforces non-negotiable guardrails, guiding users on data integrity, proper encoding (e.g., handling uncertainty, missing values), and accessibility standards (WCAG). Supports advanced plotting libraries like Matplotlib and Seaborn, focusing on methodological rigor over mere aesthetics.