Search Skills

Coordinates collaborative solution design planning using multiple AI models (Codex, Claude, Gemini, OpenCode) with Codex as coordinator.

Fetches the latest response from Google's Gemini AI model via CLI command, triggered only by explicit user request.

Enables asynchronous delegation to Gemini AI model with immediate turn termination upon user command.

Guides developers in implementing FlowMVI for state management, including stores, containers, plugin pipelines, and decorators.

Executes bulk RNA-seq differential expression analysis using omicverse, including DESeq2 normalization, statistical testing, and pathway enrichment.

Preprocesses TCGA bulk RNA-seq data including expression, clinical, and sample metadata using omicverse, generating annotated AnnData files for downstream analysis.

Performs statistical tests, hypothesis testing, and correlation analysis using scipy and statsmodels for data-driven insights.

Maps scRNA-seq atlases to spatial transcriptomics slides using deep-forest AI training and marker visualization for genomic analysis.

Converts bulk RNA-seq data into synthetic single-cell datasets using Bulk2Single workflow for cell fraction estimation and quality control against reference scRNA-seq data.

Provides a checklist reference for single-cell RNA-seq downstream analysis including AUCell scoring and metacell differential expression using OmicVerse.

Guides users in utilizing OmicVerse's visualization tools for bulk and single-cell omics data, featuring venn/volcano charts and palette selection.

Executes gene set enrichment analysis (GSEA) with proper gene set formatting and pathway database integration for OmicVerse.

Exports data analysis results and tables to Excel format using openpyxl, compatible with any LLM provider.

Analyzes protein-protein interactions by querying STRING database, building PPI graphs with pyPPI, and generating styled network visualizations for gene lists.

Infers ligand-receptor communication networks from single-cell RNA-seq data using CellPhoneDB v5 and generates CellChat-style visualizations.

Creates publication-quality data visualizations using matplotlib and seaborn, compatible with any LLM provider for seamless integration.

Generates intermediate cells for scRNA-seq developmental trajectories using bulk RNA-seq data and AI models (beta-VAE, GNN) for trajectory interpolation.

Guides single-cell RNA-seq data preprocessing with OmicVerse, covering QC, normalization, HVG detection, and dimensionality reduction across CPU/GPU configurations.

Quick-reference guide for OmicVerse tutorials on multi-omics integration techniques including MOFA, SIMBA, and TOSICA for single-cell data analysis.

Transforms, cleans, and reshapes data using pandas and numpy, compatible with any LLM provider for AI/ML pipelines.

Executes bulk RNA-seq differential expression analysis with PyDESeq2, including ID mapping, DE testing, fold-change thresholding, and enrichment visualization.

Guides AI-powered annotation workflows for single-cell omics data using tools like CellVote, GPTAnno, and weighted KNN transfer.

Generates professional PDF reports with text, tables, and images using reportlab, compatible with any LLM provider for content integration.

Guides execution of single-cell clustering and batch correction via omicverse, covering preprocessing, QC, multimethod clustering, and cross-batch integration.