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Combine scTCR/BCR repertoire data with scRNA-seq expression data using `scRepertoire::combineExpression()`. This process integrates immune receptor information (CDR3 sequences, V(D)J genes, clonotypes) into a S...
Performs fine-grained re-clustering on specific subsets of cells (e.g., individual clusters, cell types, or custom subsets). Unlike `Seurat::FindSubCluster` which only finds subclusters within a single cluster,...
Identifies and visualizes the top expressing genes per cluster in T/B cells, followed by pathway enrichment analysis. Provides quick cluster characterization by highlighting the most highly expressed genes and...
TESSA (TCR and Expression Joint Clustering) is a Bayesian model that integrates T-cell receptor (TCR) sequence profiling with transcriptomes of T cells. It maps the functional landscape of the TCR repertoire by...
Load, prepare, and apply quality control (QC) to single-cell RNA-seq data using Seurat. Performs data loading, QC filtering, normalization, and multi-sample integration. This is a core preprocessing process tha...
Load single-cell TCR-seq or scBCR-seq data from various formats into a scRepertoire-compatible object. This process reads VDJ (variable, diversity, joining) receptor contig data from multiple single-cell sequen...
Separates T and non-T cells or B and non-B cells from a mixed cell population. Uses either clonotype percentage from VDJ data, indicator gene expression (CD3 markers for T cells, CD19/CD20 for B cells), custom...
Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters....
Imputes missing/dropout values in scRNA-seq expression data to improve metabolic pathway analysis. This process handles sparsity common in single-cell RNA sequencing data by filling in zero values using advance...
Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assig...
Comprehensive metabolic landscape analysis pipeline for scRNA-seq data. This is an all-in-one process group performing complete metabolic pathway analysis including expression imputation, feature selection, pat...
Performs coarse clustering on ALL cells (including T cells, B cells, and non-T/B cells) before cell type selection. This process identifies broad cell populations to enable subsequent T/B cell selection via `TO...
Finds marker genes for clusters of ALL cells before T/B cell selection. This process identifies differentially expressed genes across unsupervised clusters to help identify broad cell types (T cells, B cells, M...
Finds differentially expressed genes (markers) for clusters of T/B cells using Seurat's FindMarkers function. Performs statistical testing between clusters, identifies cluster-defining genes, and automatically...
Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR. Aggregates single-cell counts to sample-level pseudo-bulk data, then identifies differentially expressed genes between condition...
Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathway...
Infer ligand-receptor interactions and cell-cell communication networks from single-cell RNA-seq data using the LIANA+ framework. Identifies potential signaling events between cell types based on gene expressio...
Design principles for multi-agent workflows with validation gates using the Ralph Wiggum technique. Use when orchestrating multiple AI agents (Opus/Sonnet/Gemini/Codex) for complex autonomous development tasks...
Execute evidence-based decision-making through 6-stage causal flow. Use for known processes, operational execution, and decisions with clear cause-effect chains.
Track goal progress, derive state from execution, identify gaps, trigger actions. Use for goal status checks, progress reviews, and operational goal management.
Transform objectives into structured goals with plans. Use when user declares intent ("I want to...", "Goal is to...", "Achieve X by Y"). Decomposes into subgoals, milestones, success criteria.
iOS HealthKit data sync CLI commands and patterns. Use when working with healthsync CLI, fetching Apple Health data (steps, heart rate, sleep, workouts), pairing iOS devices over local network, or understanding...
Creates and reviews CLAUDE.md configuration files for Claude Code. Applies HumanLayer guidelines including instruction budgets (~50 user-level, ~100 project-level), WHAT/WHY/HOW framework, and progressive discl...
Use when the user asks for recommendations, next steps, best approaches, or “what should I do” guidance to achieve a goal or fix a problem.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.