Skill UI

Provides systematic Power BI performance troubleshooting guidance covering model optimization, DAX design, report layout, and capacity diagnostics, plus tool-driven measurement tips for faster page loads, interactions, and query execution.

Guides Power BI visualization and UX decisions—capturing business context, selecting optimal charts, specifying layouts, color, typography, and interaction patterns to create accessible, insight-driven dashboards across audiences.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

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.

A powerful tool designed for academic researchers and scientists. It searches a curated database of scientific literature and retrieves structured metadata and quantitative experimental data (including methods, results, sample sizes, and quality scores) directly from full-text papers, going far beyond simple abstracts. Ideal for systematic literature reviews, evidence synthesis, and meta-analyses requiring detailed, comparable study information.

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.

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

Matchms processes LC-MS/MS spectra with import/export tools, filtering, and multiple similarity scorers so you can identify metabolites, match libraries, and automate reproducible workflows.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.