Skill UI

Automates Luigi task generation for data pipelines, offering guided best practices, code and configuration outputs, and validation to streamline ETL, transformation, orchestration, and streaming workflows.

Automates ROC curve plotting tasks in ML training, guiding data prep, model training, tuning, tracking, and code generation for production-ready results.

High-performance Rust toolkit with Python bindings for genomic interval analysis, covering overlap detection, coverage track generation, ML tokenization, reference sequence handling, fragment processing, and scoring workflows for sequencing datasets.

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

This skill automates the generation of publication-quality figures and comparison tables for academic papers. It converts structured experimental data (e.g., JSON, CSV) into various plots, including line graphs, bar charts, scatter plots, and heatmaps, ensuring consistent styling suitable for LaTeX submissions. It handles the core data visualization needs for ML and research reports, greatly streamlining the process of creating professional results sections.

The Stock Analyzer Skill provides comprehensive technical analysis for stocks and ETFs. It utilizes industry-standard indicators like RSI, MACD, and Bollinger Bands to generate actionable trading signals. Users can perform advanced technical comparisons, monitor portfolio strength, and identify chart patterns through natural language queries, eliminating the need for manual indicator calculation.

This skill provides a comprehensive workflow to transform raw data into professional visual charts. It intelligently analyzes data features (e.g., trends, comparisons, flows) to select the optimal chart type (line, bar, pie, etc.). Users provide data and specifications, and the skill generates a high-quality, detailed chart image using a structured JavaScript process.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

Gtars is a high-performance toolkit written in Rust with Python bindings, specialized for computational genomics. It provides advanced functionality for manipulating genomic interval data, including overlap detection (IGD), coverage track generation (BigWig), and genomic tokenization for machine learning preprocessing. Ideal for researchers working with BED files, single-cell data, and ML-driven biological models.

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.