Skill UI

This coach is designed to systematically develop creative thinking and problem-solving skills. It guides users through established methodologies such as Design Thinking, SCAMPER, TRIZ, and Lateral Thinking. It teaches users how to move beyond initial ideas, overcome creative blocks, and apply structured frameworks to generate, refine, and implement novel solutions across various domains. Ideal for students, professionals, and entrepreneurs facing complex challenges.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

COBRApy is a Python library for constraint-based reconstruction and analysis of genome-scale metabolic models, letting you load SBML/JSON/YAML models, perform FBA/FVA/sampling, and study gene or reaction knockouts for systems biology and metabolic engineering.

Dask is a Python library that scales pandas/NumPy workflows beyond RAM by parallelizing DataFrames, Arrays, Bags, and custom tasks across cores or clusters for ETL, log analysis, and distributed ML.

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

Histolab streamlines whole-slide microscopy by automating tissue detection, tile extraction, and stain normalization for H&E scans, making it ideal for lightweight dataset prep and quick tile-based analysis.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

Provides comprehensive guidance on determining the optimal scaling strategy for Qdrant vector databases. Covers when to transition from vertical to horizontal scaling, how to select the right number of nodes and shards, and best practices for setting replication factors to ensure zero-downtime operation and fault tolerance. Also details proper shard management and common scaling pitfalls to avoid.

A comprehensive guide for optimizing query latency in Qdrant. This resource explains advanced performance tuning techniques, focusing on critical resource management like RAM utilization, segment count adjustments, and HNSW parameter tuning. It details strategies for scaling, quantization, and hardware considerations (e.g., local NVMe) to ensure consistently low-latency search performance, and outlines common pitfalls to avoid.

Generate production-quality React components and UIs using the specialized Rayden UI component library and Tailwind CSS. This skill ensures that all generated code strictly adheres to a defined design system, including correct component usage, design tokens, prop validation, and established layout patterns. Ideal for scaffolding complex pages like dashboards, login forms, and data tables while maintaining premium aesthetics and consistency.