Search Skills

Generates and validates MCPBR configuration files for server benchmarking setup and performance testing.

Provides a quick-start command to run SWE-bench Lite, an AI evaluation benchmark for assessing code generation and software engineering capabilities.

Manages Linear issues, projects, and team workflows for efficient task tracking and status updates.

Clusters TCR/BCR clones by CDR3 sequences using Faiss-based algorithms, adding a cluster column for clonotype analysis in immunology.

Executes fast Gene Set Enrichment Analysis (GSEA) on single-cell RNA-seq data via fgsea R package, identifying enriched pathways and producing publication-ready visualizations.

Identifies differentially expressed marker genes across unsupervised cell clusters to determine broad cell types in immune cell populations.

Initial coarse clustering of all cell types (T, B, non-T/B) to identify broad populations before T/B cell selection in single-cell RNA-seq analysis.

Maps single-cell RNA-seq query datasets to reference atlases via Seurat's workflow, enabling label transfer and integration without modifying query data.

Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR, aggregating single-cell data to identify differentially expressed genes while accounting for experimental design complexities.

Analyzes physicochemical properties of CDR3 amino acid sequences using regression to compare T-cell receptor repertoires between cell groups.

Annotates single-cell clusters with biological cell types using AI-driven methods including CellTypist and ScType for data interpretation.

Generates clonality statistics and diversity visualizations for TCR/BCR repertoire analysis, including Shannon, Simpson, and Gini metrics.

Separates T/B cells from mixed populations using VDJ data, marker genes, or k-means clustering for immunological analysis.

Integrates scTCR/BCR repertoire data with scRNA-seq expression data into Seurat objects for clonotype-aware gene expression analysis.

Performs metabolic pathway enrichment analysis using GSEA-based methods with fgsea, generating visualizations for biological interpretation across cell groups.

Performs unsupervised clustering on single-cell RNA-seq data using Seurat, including UMAP visualization and Louvain/Leiden algorithms for cell cluster identification.

Loads and processes single-cell TCR-seq and scBCR-seq data from multiple formats into scRepertoire-compatible objects for scRNA-seq integration.

Visualizes cell-cell communication inference results with network diagrams, heatmaps, and interaction plots for interpreting ligand-receptor interactions between cell types.

Generates cluster statistics and visualizations for Seurat single-cell RNA-seq data, including dimension reduction and gene expression plots.

Analyzes metabolic pathway heterogeneity in cell populations using NES, PCA, and GSEA to quantify diversity and identify subpopulation-specific metabolic states.

Identifies top-expressed genes per cell cluster and conducts pathway enrichment analysis to visualize gene expression patterns and biological functions across cell populations.

Comprehensive metabolic pathway analysis pipeline for scRNA-seq data, including expression imputation, feature selection, and heterogeneity analysis.

Automates differential gene expression analysis and pathway enrichment for single-cell RNA-seq clusters, generating publication-ready visualizations.

Enables fine-grained re-clustering on arbitrary cell subsets using full clustering workflows (PCA, UMAP, etc.) beyond standard subclustering methods.