Skill UI

The ESM skill provides comprehensive tools for computational biology, leveraging ESM3 and ESM-C models. It enables sophisticated protein sequence generation, structure prediction (sequence to 3D structure), inverse folding (structure to sequence design), and generating function-conditioned proteins. Use it for de novo protein design, feature embedding, and advanced structural analysis in bioinformatics workflows.

This skill performs comprehensive Exploratory Data Analysis (EDA) on specialized scientific datasets. It supports over 200 file formats across diverse domains, including chemistry (PDB), bioinformatics (FASTA, BAM), microscopy (TIFF), and spectroscopy. The system automatically detects the file type, assesses data quality and integrity, and generates detailed, format-specific markdown reports, providing deep insights and recommending downstream analyses.

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.

Gtars is a high-performance toolkit written in Rust with Python bindings, designed for comprehensive analysis of genomic interval data. It provides specialized modules for overlap detection, generating coverage tracks (WIG/BigWig), tokenization for Machine Learning models, and detailed fragment analysis in single-cell genomics. Use it for robust bioinformatics pipelines and computational genomics research.

LaminDB is an open-source, lineage-native lakehouse specifically designed for complex biological datasets and models. It provides unified capabilities for querying, tracking data lineage, validating schemas, and ensuring FAIR compliance across diverse data types (e.g., AnnData, Parquet, Zarr). Use this skill when managing multi-modal genomic data, tracking computational workflows (like Nextflow/Snakemake), or building robust, reproducible data infrastructure in bioinformatics.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.

A high-performance library built on Polars for advanced genomic interval operations and bioinformatics file I/O. It provides robust support for core interval arithmetic (overlap, merge, complement) and reading/writing major formats like VCF, BAM, BED, and GFF. Features include out-of-core streaming, cloud-native I/O (S3, GCS, Azure), and a powerful SQL interface, making it ideal for large-scale genomic research.

Pysam is a comprehensive Python toolkit for handling genomic data. It provides a Pythonic interface for reading, writing, and manipulating essential genomic formats, including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences. It is critical for developing robust bioinformatics pipelines, performing coverage analysis, and analyzing genetic variations.

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust capabilities for sequence manipulation (DNA, RNA, Protein), performing complex sequence alignments (pairwise and multiple), constructing and analyzing phylogenetic trees, and calculating diverse ecological metrics. It is essential for microbiome analysis, evolutionary studies, and general biological data processing, supporting standard formats like FASTA, Newick, and BIOM.

Nextflow is a powerful workflow language and runtime for building reproducible, portable, and scalable data pipelines, especially dominant in bioinformatics. This skill covers the entire workflow lifecycle: from authoring complex pipelines using the modern DSL2 syntax, to configuring containerized execution (Docker, Singularity), scaling to High-Performance Computing (HPC) or cloud environments, and running/debugging existing nf-core workflows.