Skill UI

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

A comprehensive guide and expert resource for mastering Julia 1.10+ development. Covers advanced topics including multiple dispatch, performance optimization, type stability, and modern tooling. Ideal for building production-ready applications in scientific computing, machine learning, data science, and web development.

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.

Analyzes comprehensive fitness logs to track quantitative trends in volume, intensity, and frequency. It tracks progress across multiple disciplines (running, strength) and correlates exercise metrics with vital health indicators like blood pressure and weight, providing personalized, science-backed training and recovery advice.

This skill provides expert guidance on applied behavioral science for marketing. It helps users analyze conversion roadblocks, optimize pricing, and improve copywriting by applying 70+ established psychological principles and mental models. Ideal for CRO specialists, marketers, and product managers seeking data-backed, psychologically informed strategies.

This skill performs in-depth literature reviews across complex communications domains, including 5G/6G, Wi-Fi, satellite networking, and advanced protocols. It systematically searches user-defined knowledge bases (Zotero, Obsidian, local files) and major academic databases (IEEE, ACM, ScienceDirect) to provide comprehensive survey summaries, related work analyses, and technical landscape overviews.

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.

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.

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.