Skill UI

A comprehensive coaching guide for advancing careers in the technology industry, covering roles like Software Engineering, Data Science, and Product Management. It provides actionable strategies for resume optimization (ATS format), technical interview preparation (Algorithms, System Design, Behavioral), salary negotiation, and navigating complex career paths (IC vs. Management). It adapts advice to both global (FAANG) and Chinese tech ecosystems.

An evidence-based coach providing guidance on fundamental wellness pillars: sleep hygiene, nutrition, exercise, and stress management. It uses behavior change science and motivational interviewing principles to help users build sustainable, lasting health habits. This tool is educational and motivational, designed to support general wellness journeys, and always emphasizes the critical need to consult healthcare professionals for diagnosis or treatment.

This skill provides comprehensive integration with the Benchling cloud platform using the Python SDK and REST API. It allows users to programmatically manage critical life sciences data, including registry entities (DNA/RNA/proteins), inventory tracking, ELN entries, workflows, and querying the Data Warehouse. Ideal for automating complex lab data pipelines and syncing Benchling with external systems.

A comprehensive Python package providing a unified interface to over 40 major bioinformatics web services and databases. Use it for complex biological workflows, including cross-database querying (e.g., UniProt, KEGG, ChEMBL), identifier mapping, protein sequence retrieval, pathway analysis, and running sequence similarity searches like BLAST. It streamlines data integration from multiple life science resources into consistent Python code.

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.

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.

A comprehensive toolkit for advanced geospatial analysis. It covers remote sensing (Sentinel, Landsat), GIS operations (vector/raster/point cloud), spatial statistics, and Machine Learning for Earth Observation. Supports cloud-native workflows, multiple data types, and 8 programming languages, making it ideal for complex scientific research and commercial spatial projects.

A powerful Python library that extends pandas to enable complex spatial operations on vector geometric data. It supports reading and writing diverse formats, including Shapefile, GeoJSON, and GeoPackage. Users can perform advanced GIS tasks such as spatial joins, buffering, overlay analysis, calculating areas, managing Coordinate Reference Systems (CRS), and generating interactive choropleth maps, making it essential for geographic data science projects.

NetworkX is a powerful Python library used for creating, manipulating, and analyzing complex networks and graphs. It supports graph structures (directed/undirected, multi-edges) and provides extensive algorithms for tasks such as finding shortest paths, calculating centrality measures (e.g., PageRank), detecting communities, and simulating various network types (e.g., scale-free, small-world). Ideal for social science, biology, and transportation network modeling.

An end-to-end toolkit designed for analyzing high-density Neuropixels electrophysiology recordings. It implements best practices using SpikeInterface, covering the entire workflow: data loading (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4), quality metric computation, and unit curation (including AI-assisted review). Ideal for neuroscience research.

Pymatgen is a comprehensive Python toolkit designed for computational materials science. It enables users to create, analyze, and manipulate complex crystal structures, calculate electronic properties (DOS, band structures), simulate phase transitions, and interact with large material databases like the Materials Project. Ideal for solid-state physics, chemistry, and advanced materials research.

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.