Skill UI

COBRApy is a powerful Python library for Constraint-Based Reconstruction and Analysis (COBRA). It enables deep dives into cellular metabolism, allowing users to build, simulate, and analyze complex metabolic models (e.g., genome-scale models). Core functionalities include Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), performing gene/reaction knockouts, and optimizing growth media. It supports standard metabolic model formats like SBML, JSON, and YAML.

This tool opens the dedicated Context Mode Insight dashboard in your default browser. Insight serves as the central analytics layer for AI-assisted engineering teams, providing crucial metrics such as per-engineer productive rates, identifying retry waste, detecting blockers, and offering role-narrowed performance views. It is designed to be the single source of truth for comprehensive team performance monitoring and sign-in details.

Dask is a powerful Python library designed for parallel and distributed computing. It allows users to scale existing pandas and NumPy workflows beyond the limits of available RAM, processing massive datasets up to the terabyte scale. It provides specialized APIs (DataFrames, Arrays, Bags) for handling structured, scientific, and unstructured data efficiently across single machines or multi-node clusters.

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.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

DeepChem is a comprehensive Python library designed for applying machine learning to complex fields like chemistry, materials science, and biology. It enables users to perform molecular property prediction (e.g., ADMET, toxicity), train models using specialized graph neural networks (GNNs), and process diverse molecular formats (SMILES, SDF, FASTA). It provides extensive featurization options, making it foundational for drug discovery and computational chemistry research.

DeepTools is a comprehensive command-line toolkit for processing and analyzing high-throughput sequencing data. It supports workflows for ChIP-seq, RNA-seq, ATAC-seq, and other NGS experiments. Core capabilities include converting BAM files to normalized coverage tracks (bigWig), performing quality control (QC), sample correlation analysis, and generating publication-quality visualizations, such as heatmaps and profile plots across genomic regions.

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.

This advanced tool utilizes the Digital Human DNA (DHDNA) framework to extract a comprehensive cognitive signature from any piece of text. It analyzes the author's unique thinking patterns, reasoning methods, decision-making styles, and core values across 12 detailed dimensions (e.g., Analytical Depth, Ethical Reasoning, Emotional Processing). It provides a structured profile, revealing the topology of the mind behind the writing, without judgment.

ETE is a comprehensive toolkit for phylogenomic research, designed to handle and analyze hierarchical tree structures. It supports various tree formats (Newick, NHX) and provides advanced functionalities such as detecting evolutionary events (duplication/speciation), identifying orthologs/paralogs, integrating with NCBI taxonomy for lineage tracking, and generating publication-quality visualizations in PDF/SVG.

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.

A lightweight Python library designed for reading, parsing, and writing Flow Cytometry Standard (FCS) files. It extracts event data as NumPy arrays, handles complex metadata, and supports multiple FCS versions (2.0-3.1). Ideal for data preprocessing, building cytometry pipelines, and inspecting experimental results.