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Claude Skills by FreedomIntelligence
github.com/FreedomIntelligence741 skills2 installs1,922 views
- Bio Clinical Databases Clinvar LookupQuery ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Dbsnp QueriesQuery dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Gnomad FrequenciesQuery gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Hla TypingCall HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Myvariant QueriesQuery myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases PharmacogenomicsQuery PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Polygenic RiskCalculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Somatic SignaturesExtract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Tumor Mutational BurdenCalculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.Votes: 0GitHub stars: 2,984
- Bio Clinical Databases Variant PrioritizationFilter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.Votes: 0GitHub stars: 2,984
- Bio Clip Seq Binding Site Annotation--> --- name: bio-clip-seq-binding-site-annotation description: Annotate CLIP-seq binding sites to genomic features including 3'UTR, 5'UTR, CDS, introns, and ncRNAs. Use when characterizing where an RBP binds in transcripts. tool_type: mixed primary_tool: ChIPseeker measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Clip Seq Clip Alignment--> --- name: bio-clip-seq-clip-alignment description: Align CLIP-seq reads to the genome with crosslink site awareness. Use when mapping preprocessed CLIP reads for peak calling. tool_type: cli primary_tool: STAR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Clip Seq Clip Motif Analysis--> --- name: bio-clip-seq-clip-motif-analysis description: Identify enriched sequence motifs at CLIP-seq binding sites for RBP binding specificity. Use when characterizing the sequence preferences of an RNA-binding protein. tool_type: cli primary_tool: HOMER measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Clip Seq Clip Peak Calling--> --- name: bio-clip-seq-clip-peak-calling description: Call protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads. tool_type: cli 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: 2,984
- Bio Clip Seq Clip Preprocessing--> --- name: bio-clip-seq-clip-preprocessing description: Preprocess CLIP-seq data including adapter trimming, UMI extraction, and PCR duplicate removal. Use when preparing raw CLIP, iCLIP, or eCLIP reads for peak calling. tool_type: cli primary_tool: umi_tools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Codon Usage--> --- name: bio-codon-usage description: Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis. tool_type: python primary_tool: Bio.SeqUtils.CodonUsage measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Analyze codon usage patterns and calculate codon ada...Votes: 0GitHub stars: 2,984
- Bio Comparative Genomics Ancestral Reconstruction--> --- name: bio-comparative-genomics-ancestral-reconstruction description: Reconstruct ancestral sequences at phylogenetic nodes using PAML and IQ-TREE marginal likelihood methods. Infer ancient protein sequences and trace evolutionary trajectories through sequence history. Use when inferring ancestral states for protein resurrection or tracing evolutionary history. tool_type: mixed primary_tool: PAML measurable_outcome: Execute skill workflow successfully with valid output within 15 minute...Votes: 0GitHub stars: 2,984
- Bio Comparative Genomics Hgt Detection--> --- name: bio-comparative-genomics-hgt-detection description: Detect horizontal gene transfer events using HGTector, compositional analysis, and phylogenetic incongruence methods. Identify foreign genes in bacterial and archaeal genomes from anomalous composition or unexpected phylogenetic placement. Use when searching for horizontally transferred genes or analyzing genome evolution in prokaryotes. tool_type: mixed primary_tool: HGTector measurable_outcome: Execute skill workflow successf...Votes: 0GitHub stars: 2,984
- Bio Comparative Genomics Ortholog Inference--> --- name: bio-comparative-genomics-ortholog-inference description: Infer orthologous gene groups across species using OrthoFinder and ProteinOrtho. Identify orthologs, paralogs, and co-orthologs for comparative genomics and functional annotation transfer. Use when identifying gene orthologs across species or building orthogroups for evolutionary analysis. tool_type: cli primary_tool: OrthoFinder measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. a...Votes: 0GitHub stars: 2,984
- Bio Comparative Genomics Positive Selection--> --- name: bio-comparative-genomics-positive-selection description: Detect positive selection using dN/dS (omega) tests with PAML codeml and HyPhy. Identify sites and branches under adaptive evolution through codon models and branch-site tests. Use when testing for adaptive evolution in gene families or identifying positively selected sites. tool_type: mixed primary_tool: PAML measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read...Votes: 0GitHub stars: 2,984
- Bio Comparative Genomics Synteny Analysis--> --- name: bio-comparative-genomics-synteny-analysis description: Analyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions. tool_type: mixed primary_tool: MCScanX measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-to...Votes: 0GitHub stars: 2,984
- Bio Compressed FilesRead and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files.Votes: 0GitHub stars: 2,984
- Bio Consensus SequencesGenerate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.Votes: 0GitHub stars: 2,984
- Bio Copy Number Cnv AnnotationAnnotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.Votes: 0GitHub stars: 2,984
- Bio Copy Number Cnv VisualizationVisualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.Votes: 0GitHub stars: 2,984
- Bio Copy Number Cnvkit AnalysisDetect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.Votes: 0GitHub stars: 2,984
- Bio Copy Number Gatk CnvCall copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Base Editing AnalysisAnalyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Batch CorrectionBatch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Crispresso EditingCRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Hit CallingStatistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Jacks AnalysisJACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Library DesignCRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Mageck AnalysisMAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.Votes: 0GitHub stars: 2,984
- Bio Crispr Screens Screen QcQuality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance.Votes: 0GitHub stars: 2,984
- Bio Ctdna Mutation DetectionDetects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.Votes: 0GitHub stars: 2,984
- Bio Data Visualization Circos Plots--> --- name: bio-data-visualization-circos-plots description: Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations. tool_type: mixed primary_tool: Circos measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Circular genome visualizations for displaying m...Votes: 0GitHub stars: 2,984
- Bio Data Visualization Color Palettes--> --- name: bio-data-visualization-color-palettes description: Select and apply colorblind-friendly palettes for scientific figures using viridis, RColorBrewer, and custom color schemes. Use when selecting colorblind-friendly palettes for figures. tool_type: mixed primary_tool: viridis measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Genome Browser Tracks--> --- name: bio-data-visualization-genome-browser-tracks description: Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks. tool_type: mixed primary_tool: pyGenomeTracks measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Genome Tracks--> --- name: bio-data-visualization-genome-tracks description: Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks. tool_type: mixed primary_tool: pyGenomeTracks measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Ggplot2 Fundamentals--> --- name: bio-data-visualization-ggplot2-fundamentals description: Create publication-quality scientific figures with ggplot2 including scatter plots, boxplots, heatmaps, and multi-panel layouts. Use when creating static figures for papers, presentations, or reports in R. tool_type: r primary_tool: ggplot2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Heatmaps Clustering--> --- name: bio-data-visualization-heatmaps-clustering description: Create clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters. tool_type: mixed primary_tool: ComplexHeatmap measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_co...Votes: 0GitHub stars: 2,984
- Bio Data Visualization Interactive Visualization--> --- name: bio-data-visualization-interactive-visualization description: Create interactive HTML plots with plotly and bokeh for exploratory data analysis and web-based sharing of omics visualizations. Use when building zoomable, hoverable plots for data exploration or web dashboards. tool_type: mixed primary_tool: plotly measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Multipanel Figures--> --- name: bio-data-visualization-multipanel-figures description: Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures. tool_type: mixed primary_tool: patchwork measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Specialized Omics Plots--> --- name: bio-data-visualization-specialized-omics-plots description: Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots. tool_type: mixed primary_tool: ggplot2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Upset Plots--> --- name: bio-data-visualization-upset-plots description: Create UpSet plots to visualize set intersections as an alternative to Venn diagrams using UpSetR or upsetplot. Use when comparing overlapping gene sets, peak sets, or sample groups with more than 3 sets. tool_type: mixed primary_tool: UpSetR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio Data Visualization Volcano Customization--> --- name: bio-data-visualization-volcano-customization description: Create publication-ready volcano plots with custom thresholds, gene labels, and highlighting using ggplot2, EnhancedVolcano, or matplotlib. Use when visualizing differential expression or association results with gene annotations. tool_type: mixed primary_tool: ggplot2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 2,984
- Bio De Deseq2 BasicsPerform 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.Votes: 0GitHub stars: 2,984
- Bio De Edger BasicsPerform 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.Votes: 0GitHub stars: 2,984
- Bio De ResultsExtract, 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.Votes: 0GitHub stars: 2,984