Skill UI

DiffDock is a state-of-the-art, diffusion-based deep learning tool for predicting 3D binding poses of drug-like ligands to protein targets. It supports both PDB files and amino acid sequences for proteins, and accepts diverse ligand formats (SMILES, SDF, MOL2). It is designed for structure-based drug discovery, lead optimization, and high-throughput virtual screening campaigns, providing predicted poses and confidence scores.

A comprehensive skill for building, operating, and automating complex, reproducible genomics workloads on the DNAnexus platform. It provides deep integration with the `dx` CLI, `dxpy` Python SDK, and workflow languages like Nextflow and WDL. Use it for data transfer, job monitoring, project automation, and ensuring secure, efficient execution of large-scale biological computations.

ESM provides advanced protein language models (ESM3, ESMC) for deep biological understanding, generation, and structural prediction. This skill enables users to generate novel protein sequences with specific functions, perform structure prediction from sequence, execute inverse folding (sequence design from structure), and extract high-quality protein embeddings for downstream machine learning tasks. Ideal for drug discovery and advanced bioinformatics research.

The ETE Toolkit is a powerful Python library designed for analyzing, manipulating, comparing, and visualizing phylogenetic trees stored in Newick or Nexus formats. It allows users to perform topology edits, calculate phylogenetic distances (e.g., Robinson-Foulds), detect evolutionary events, and query external taxonomic databases like NCBI/GTDB. Note that this tool analyzes existing trees; it is not used for inferring trees from raw sequences.

FlowIO is a specialized, low-level library designed for reading, inspecting, and writing Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect detailed metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset structures. It is crucial for data preparation, format conversion, and foundational metadata management in bioinformatics pipelines, but does not perform complex analysis like gating or compensation.

A comprehensive framework for planning, configuring, executing, and analyzing complex Computational Fluid Dynamics (CFD) simulations using pseudospectral methods. It enforces a rigorous, multi-stage workflow, guiding users from defining state equations and boundary conditions to generating resource estimates, running dry-runs, and preparing site-specific MPI jobs. Designed for high-fidelity scientific computing and reproducibility.

This skill generates and edits high-quality images by utilizing various advanced AI models (including Gemini, FLUX, GPT-Image, and Seedream) through the OpenRouter API. It is ideal for creating photorealistic artwork, concept art, logos, detailed illustrations, or performing complex image compositing and editing using reference images. Supports diverse parameters like resolution, seed, and aspect ratio for precise creative control.

Geniml provides a robust framework for running machine learning and statistical workflows over genomic interval sets. It treats coordinates, assemblies, and model artifacts as explicit contracts, enabling users to validate data structures (like BED files) and plan complex runs (such as Region2Vec or scEmbed) locally. The tool emphasizes reproducibility, adherence to biological data standards, and running without unnecessary network calls.

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.

A comprehensive guide for advanced geospatial data processing using GeoPandas. This skill targets professional workflows involving planar vector data, emphasizing rigorous data provenance, coordinate reference system (CRS) management, and complex spatial operations. It details necessary safety contracts, correctness gates, and best practices for handling sensitive geographic information in reproducible Python environments.

This tool provides a conservative, resource-aware snapshot of the current process's environment, including CPU, memory, disk, and accelerator limits. It helps users plan workloads accurately by providing clear boundary constraints (cgroup, scheduler, host limits) without performing stressful tests or making assumptions about usability.

Ginkgo Cloud Lab provides remote access to an advanced autonomous lab infrastructure, enabling users to submit and manage complex molecular biology protocols. It supports diverse applications including protein expression and purification (cell-free, E. coli, Pichia), quantitative assays (HiBiT, A280, LabChip), nucleic acid synthesis (IVT mRNA/circRNA), and characterization techniques (Thermal Shift, Echo-MS, SPR). The platform features an AI agent (EstiMate) for custom workflow feasibility and pricing.