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Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,578 skills3 installs3,530 views
- Differential Splicing--> --- name: bio-differential-splicing description: Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. Use when comparing splicing patterns between treatment groups, tissues, or disease states. tool_type: mixed primary_tool: rMATS-turbo measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...Votes: 0GitHub stars: 6
- Isoform Switching--> --- name: bio-isoform-switching description: Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect protein function. tool_type: r primary_tool: IsoformSwitchAnalyzeR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - ru...Votes: 0GitHub stars: 6
- Sashimi Plots--> --- name: bio-sashimi-plots description: Creates sashimi plots showing RNA-seq read coverage and splice junction counts using ggsashimi or rmats2sashimiplot. Visualizes differential splicing events with grouped samples and junction read support. Use when visualizing specific splicing events or validating differential splicing results. tool_type: python primary_tool: ggsashimi measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read...Votes: 0GitHub stars: 6
- Single Cell Splicing--> --- name: bio-single-cell-splicing description: Analyzes alternative splicing at single-cell resolution using BRIE2 for probabilistic PSI estimation or leafcutter2 for cluster-based analysis with NMD detection. Identifies cell-type-specific splicing patterns. Use when analyzing isoform usage in scRNA-seq or finding splicing differences between cell populations. tool_type: python primary_tool: BRIE2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes...Votes: 0GitHub stars: 6
- Splicing Qc--> --- name: bio-splicing-qc description: Assesses RNA-seq data quality for splicing analysis including junction saturation curves, splice site strength scoring, and junction coverage metrics using RSeQC. Use when evaluating data suitability for splicing analysis or troubleshooting low event detection. tool_type: python primary_tool: RSeQC measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Assess RNA...Votes: 0GitHub stars: 6
- Splicing Quantification--> --- name: bio-splicing-quantification description: Quantifies alternative splicing events (PSI/percent spliced in) from RNA-seq using SUPPA2 from transcript TPM or rMATS-turbo from BAM files. Calculates inclusion levels for skipped exons, alternative splice sites, mutually exclusive exons, and retained introns. Use when measuring splice site usage or isoform ratios from RNA-seq data. tool_type: python primary_tool: SUPPA2 measurable_outcome: Execute skill workflow successfully with valid ...Votes: 0GitHub stars: 6
- Batch Correction--> --- name: bio-differential-expression-batch-correction description: Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data. tool_type: r primary_tool: sva measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- De Results--> --- name: bio-de-results description: Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results. tool_type: r primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run...Votes: 0GitHub stars: 6
- De Visualization--> --- name: bio-de-visualization description: Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results. tool_type: r primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Create visualizations for differenti...Votes: 0GitHub stars: 6
- Deseq2 Basics--> --- name: bio-de-deseq2-basics description: Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2. tool_type: r primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- D...Votes: 0GitHub stars: 6
- Edger Basics--> --- name: bio-de-edger-basics description: Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR. tool_type: r primary_tool: edgeR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell...Votes: 0GitHub stars: 6
- Timeseries De--> --- name: bio-differential-expression-timeseries-de description: Analyze time-series RNA-seq data using limma voom with splines, maSigPro, and ImpulseDE2. Identify genes with dynamic expression patterns. Use when analyzing time-series or longitudinal expression data. tool_type: r primary_tool: limma measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Identify genes with significant temporal express...Votes: 0GitHub stars: 6
- Counts Ingest--> --- name: bio-expression-matrix-counts-ingest description: Load gene expression count matrices from various formats including CSV, TSV, featureCounts, Salmon, kallisto, and 10X. Use when importing quantification results for downstream analysis. tool_type: python primary_tool: pandas measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Gene Id Mapping--> --- name: bio-expression-matrix-gene-id-mapping description: Convert between gene identifier systems including Ensembl, Entrez, HGNC symbols, and UniProt. Use when mapping IDs for pathway analysis or matching different data sources. tool_type: mixed primary_tool: biomaRt measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Metadata Joins--> --- name: bio-expression-matrix-metadata-joins description: Merge sample metadata with count matrices and add gene annotations. Use when preparing data for differential expression analysis or visualization. tool_type: mixed primary_tool: pandas measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Sparse Handling--> --- name: bio-expression-matrix-sparse-handling description: Work with sparse matrices for memory-efficient storage of count data. Use when dealing with single-cell data or large bulk RNA-seq datasets where most values are zero. tool_type: python primary_tool: scipy.sparse measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Orf Detection--> --- name: bio-ribo-seq-orf-detection description: Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation. tool_type: mixed primary_tool: RiboCode measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Riboseq Preprocessing--> --- name: bio-ribo-seq-riboseq-preprocessing description: Preprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation. tool_type: cli primary_tool: bowtie2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Ribosome Periodicity--> --- name: bio-ribo-seq-ribosome-periodicity description: Validate Ribo-seq data quality by checking 3-nucleotide periodicity and calculating P-site offsets. Use when assessing library quality or determining read offsets for downstream analysis. tool_type: python primary_tool: Plastid measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Ribosome Stalling--> --- name: bio-ribo-seq-ribosome-stalling description: Detect ribosome pausing and stalling sites from Ribo-seq data at codon resolution. Use when studying translational regulation, identifying pause sites, or analyzing codon-specific translation dynamics. tool_type: python primary_tool: Plastid measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Translation Efficiency--> --- name: bio-ribo-seq-translation-efficiency description: Calculate translation efficiency (TE) as the ratio of ribosome occupancy to mRNA abundance. Use when comparing translational regulation between conditions or identifying genes with altered translation independent of transcription. tool_type: mixed primary_tool: riborex measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Alignment Free Quant--> --- name: bio-rna-quantification-alignment-free-quant description: Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto. tool_type: cli primary_tool: salmon measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Quantify transcript abundance directly from FASTQ reads using pseudo-alignment (kallisto) or selective alignm...Votes: 0GitHub stars: 6
- Count Matrix Qc--> --- name: bio-rna-quantification-count-matrix-qc description: Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis. tool_type: mixed primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Quality control and exploratory analys...Votes: 0GitHub stars: 6
- Featurecounts Counting--> --- name: bio-rna-quantification-featurecounts-counting description: Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR. tool_type: cli primary_tool: featureCounts measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Count reads mapping to genomic features (genes, exons) from BAM files.Votes: 0GitHub stars: 6
- Tximport Workflow--> --- name: bio-rna-quantification-tximport-workflow description: Import transcript-level quantifications from Salmon/kallisto into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when importing transcript counts into R for DESeq2/edgeR. tool_type: r primary_tool: tximport measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Import transcript-level estimates from Salmon, kal...Votes: 0GitHub stars: 6
- Differential Mirna--> --- name: bio-small-rna-seq-differential-mirna description: Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages. tool_type: r primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Mirdeep2 Analysis--> --- name: bio-small-rna-seq-mirdeep2-analysis description: Discover novel miRNAs and quantify known miRNAs using miRDeep2 de novo prediction from small RNA-seq data. Use when identifying new miRNAs or performing comprehensive miRNA profiling with discovery. tool_type: cli primary_tool: miRDeep2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Mirge3 Analysis--> --- name: bio-small-rna-seq-mirge3-analysis description: Fast miRNA quantification with isomiR detection and A-to-I editing analysis using miRge3. Use when quantifying known miRNAs quickly or analyzing isomiR variants and RNA editing. tool_type: python primary_tool: miRge3 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Smrna Preprocessing--> --- name: bio-small-rna-seq-smrna-preprocessing description: Preprocess small RNA sequencing data with adapter trimming and size selection optimized for miRNA, piRNA, and other small RNAs. Use when preparing small RNA-seq reads for downstream quantification or discovery analysis. tool_type: cli primary_tool: cutadapt measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Target Prediction--> --- name: bio-small-rna-seq-target-prediction description: Predict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs. tool_type: mixed primary_tool: miRanda measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Bioinformatics Singlecell--> --- name: bioinformatics-singlecell description: "Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat, scvi-tools. Includes workflows for MPN, hematologic malignancies, megakaryocyte biology." license: Proprietary ---Votes: 0GitHub stars: 6
- Computational Software Development--> --- name: computational-software-development description: "Full-stack computational software development for biomedical and life science applications. Use when building AI-powered research platforms, clinical decision support systems, multi-LLM ensemble architectures, RAG pipelines, single-cell analysis tools, or biomedical web applications. Includes production-ready patterns for PyTorch deep learning, Flask web apps, citation verification systems, and scientific software distribution." l...Votes: 0GitHub stars: 6
- Data Visualization Biomedical--> --- name: data-visualization-biomedical description: "Publication-quality visualizations for biomedical and genomics data. Use when creating volcano plots, heatmaps, UMAP plots, dot plots, survival curves, forest plots, or multi-panel figures. Includes scanpy, matplotlib, seaborn, plotly workflows with journal-ready aesthetics and proper statistical annotations." license: Proprietary ---Votes: 0GitHub stars: 6
- Mpn Research Assistant--> --- name: mpn-research-assistant description: "Myeloproliferative neoplasm (MPN) research expertise including JAK2/CALR/MPL mutations, myelofibrosis, polycythemia vera, essential thrombocythemia. Use for MPN literature search, driver mutation analysis, PPM1D pathway analysis, fibrosis markers, megakaryocyte biology, clinical trial data interpretation, and translational research." license: Proprietary ---Votes: 0GitHub stars: 6
- Ngs Analysis--> --- name: ngs-analysis description: "Next-generation sequencing data analysis pipelines including bulk RNA-seq, scRNA-seq preprocessing, variant calling, and quality control. Use when working with FASTQ files, alignment (STAR, BWA), quantification (featureCounts, Salmon), DESeq2/edgeR analysis, or building NGS pipelines. Supports GEO/SRA data retrieval." license: Proprietary ---Votes: 0GitHub stars: 6
- Python Package Builder--> --- name: python-package-builder description: "Build and publish professional Python packages to PyPI. Use when creating pip-installable packages, converting scripts to packages, setting up pyproject.toml/setup.py, adding CLI interfaces, writing tests, or preparing for PyPI upload. Covers bioinformatics tool packaging." license: Proprietary ---Votes: 0GitHub stars: 6
- Scientific Manuscript--> --- name: scientific-manuscript description: "High-impact scientific manuscript preparation for journals like Nature, Blood, Cell. Use when writing abstracts, introductions, methods, results, discussions, or figure legends. Includes citation management, statistical reporting standards, ICMJE guidelines, and journal-specific formatting for hematology/oncology publications." license: Proprietary ---Votes: 0GitHub stars: 6
- Cwl Workflows--> --- name: bio-workflow-management-cwl-workflows description: Create portable, standards-based bioinformatics pipelines with Common Workflow Language (CWL). Use when building workflows that need maximum portability across execution platforms, sharing pipelines with collaborators using different systems, or contributing to community workflow registries. tool_type: cli primary_tool: cwltool measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-t...Votes: 0GitHub stars: 6
- Nextflow Pipelines--> --- name: bio-workflow-management-nextflow-pipelines description: Create scalable, containerized bioinformatics pipelines with Nextflow DSL2 supporting Docker, Singularity, and cloud execution. Use when building portable pipelines with container support, running workflows on cloud platforms (AWS, Google Cloud), or leveraging nf-core community pipelines. tool_type: cli primary_tool: Nextflow measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowe...Votes: 0GitHub stars: 6
- Snakemake Workflows--> --- name: bio-workflow-management-snakemake-workflows description: Build reproducible bioinformatics pipelines with Snakemake using rules, wildcards, and automatic dependency resolution. Use when creating Python-based workflows, automating multi-step analyses with make-like dependency tracking, or running pipelines on HPC clusters with SLURM. tool_type: python primary_tool: Snakemake measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools...Votes: 0GitHub stars: 6
- Wdl Workflows--> --- name: bio-workflow-management-wdl-workflows description: Create portable bioinformatics pipelines with Workflow Description Language (WDL) using Cromwell or miniwdl execution engines. Use when running GATK best practices pipelines, working with Terra/AnVIL platforms, or building workflows for cloud execution on Google Cloud or AWS. tool_type: cli primary_tool: cromwell measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_fi...Votes: 0GitHub stars: 6
- Atacseq Pipeline--> --- name: bio-workflows-atacseq-pipeline description: End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility. tool_type: mixed primary_tool: MACS3 workflow: true depends_on: - read-qc/fastp-workflow - read-alignment/bowtie2-alignment - alignment-files/duplicate-handling - at...Votes: 0GitHub stars: 6
- Biomarker Pipeline--> --- name: bio-workflows-biomarker-pipeline description: End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data. tool_type: python primary_tool: sklearn workflow: true depends_on: - machine-learning/biomarker-discovery - machine-learning/model-validation - ...Votes: 0GitHub stars: 6
- Chipseq Pipeline--> --- name: bio-workflows-chipseq-pipeline description: End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3, and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation. tool_type: mixed primary_tool: MACS3 workflow: true depends_on: - read-qc/fastp-workflow - read-alignment/bowtie2-alignment - alignment-files/duplicate-handling - chip-seq/peak-calling - chip-seq/peak-annotation - c...Votes: 0GitHub stars: 6
- Clip Pipeline--> --- name: bio-workflows-clip-pipeline description: End-to-end CLIP-seq analysis from FASTQ to binding sites and motif enrichment. Use when analyzing protein-RNA interactions from CLIP-based methods. tool_type: mixed primary_tool: CLIPper measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 6
- Cnv Pipeline--> --- name: bio-workflows-cnv-pipeline description: End-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing data. tool_type: mixed primary_tool: CNVkit workflow: true depends_on: - copy-number/cnvkit-analysis - copy-number/cnv-visualization - copy-number/cnv-annotation qc_checkpoints: - after_coverage: "Uniform coverage across targets" ...Votes: 0GitHub stars: 6
- Crispr Editing Pipeline--> --- name: bio-workflows-crispr-editing-pipeline description: End-to-end CRISPR experiment design from target selection to delivery-ready constructs. Covers guide RNA design, off-target assessment, and specialized editing strategies including knockouts, base editing, and HDR knockins. Use when designing complete CRISPR editing experiments for gene knockout, correction, or tagging. tool_type: mixed primary_tool: crisprscan workflow: true depends_on: - genome-engineering/grna-design - genome...Votes: 0GitHub stars: 6
- Crispr Screen Pipeline--> --- name: bio-workflows-crispr-screen-pipeline description: End-to-end CRISPR screen analysis from FASTQ to hit genes. Orchestrates guide counting, QC, statistical analysis with MAGeCK, and hit calling with multiple methods. Use when analyzing pooled CRISPR screens from count data to hit calling. tool_type: mixed primary_tool: MAGeCK workflow: true depends_on: - crispr-screens/screen-qc - crispr-screens/mageck-analysis - crispr-screens/hit-calling - crispr-screens/library-design - crispr-...Votes: 0GitHub stars: 6
- Cytometry Pipeline--> --- name: bio-workflows-cytometry-pipeline description: End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end. tool_type: r primary_tool: CATALYST workflow: true depends_on: - flow-cytometry/fcs-handling - flow-cytometry/compensation-transformation - flow-cytometry/gating-analysis - flow-cytometry/clus...Votes: 0GitHub stars: 6
- Expression To Pathways--> --- name: bio-workflows-expression-to-pathways description: Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment. tool_type: r primary_tool: clusterProfiler workflow: true depends_on: - pathway-analysis/go-enrichment - pathway-analysis/kegg-pathways - pathway-analysis/reactome-pathways - pathway-analysis/gsea - pathway-analysis/enrichmen...Votes: 0GitHub stars: 6