Skill UI

A comprehensive guide to optimizing deep learning workflows and maximizing efficiency on cloud GPU platforms like Vast.ai. Learn techniques for selecting optimal instances by performance-per-dollar, reducing startup latency, tuning data pipelines (DataLoader, pin_memory), and scaling multi-GPU training using PyTorch and Bash scripting.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

Integrate with the DNAnexus cloud genomics platform to build apps/applets, manage FASTQ/BAM/VCF data, run workflows, and script automations via dxpy for biomedical pipelines.

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

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.

A comprehensive cloud platform for biomedical data analysis and genomics research. This skill provides tools for building, deploying, and managing complex bioinformatics pipelines (apps/applets). Users can manage diverse data types (FASTQ, BAM, VCF), execute advanced workflows, and interact with the platform programmatically using the dxpy Python SDK.

A powerful skill covering geospatial science, including GIS, remote sensing, spatial statistics, and machine learning for Earth observation. It supports processing of various data types (raster, vector, point cloud) from sources like Sentinel and Landsat, and integrates modern cloud-native workflows (STAC, COG). Ideal for environmental monitoring, urban planning, and advanced spatial modeling tasks.

Latch is a powerful Python framework designed for building and deploying scalable, serverless bioinformatics pipelines. It allows users to define complex workflows using Python decorators, seamlessly integrate standard tools like Nextflow and Snakemake, and manage cloud data through LatchFile/LatchDir abstractions. Featuring advanced resource configuration, GPU support, and pre-verified pipelines for tasks such as RNA-seq and AlphaFold, it ensures high reproducibility and efficiency in computational biology.

Rowan is a comprehensive, cloud-native platform accessible via a Python API for molecular simulation and medicinal chemistry. It streamlines complex drug discovery workflows, including pKa prediction, conformer searching, molecular docking (single and analogue series), protein-ligand cofolding, and molecular dynamics. It eliminates the need for local HPC/GPU infrastructure, making programmatic batch screening and multi-step chemistry pipelines highly scalable and reliable.

A comprehensive guide for executing core Snowflake data loading workflows. This covers setting up stages (S3, GCS, Azure), defining file formats (CSV, JSON, Parquet), and using COPY INTO for bulk loading. It also details advanced continuous ingestion using Snowpipe, enabling real-time data synchronization from cloud storage into Snowflake tables.

Nextflow is a powerful workflow language and runtime for building reproducible, portable, and scalable data pipelines. It is dominant in bioinformatics, enabling complex dataflow programming and managing workflows across diverse environments, including local machines, HPC clusters (SLURM), and cloud platforms (AWS, Azure). This skill covers the full lifecycle: running established pipelines (like nf-core), developing custom modular workflows, configuring containerization, and ensuring computational reproducibility.