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Claude Skills by BioTender-max
github.com/BioTender-max897 skills8 installs817 views
- Clinicaltrials Landscape- **Map competitive landscape** across therapeutic mechanisms for any disease - **Track specific mechanism classes** (e.g., anti-IL23, anti-TL1A, JAK inhibitors) - **Identify sponsors** and their pipeline positions by phase - **Phase distribution analysis** for business development diligence - **Pipeline monitoring** for a specific sponsor's disease portfolio - **Pre-built disease configs** available (IBD with 14 mechanism classes); generic mode for any other diseaseVotes: 0GitHub stars: 171
- Coexpression NetworkBuild weighted gene co-expression networks to identify modules of coordinately expressed genes and discover hub genes that may be key regulators. This workflow uses WGCNA (Weighted Gene Co-expression Network Analysis) to group genes into modules based on their expression patterns across samples, then correlates these modules with experimental conditions or traits.Votes: 0GitHub stars: 171
- Disease Progression LongitudinalUse this skill when you have **longitudinal patient omics data** and want to: - ✅ Reconstruct disease progression trajectories from time-series data - ✅ Order samples by disease stage (pseudotime) with irregular sampling - ✅ Identify biomarkers changing along disease trajectory - ✅ Stratify patients as fast vs. slow progressors - ✅ Predict clinical outcomes from trajectory position - ✅ Validate computational staging against clinical measuresVotes: 0GitHub stars: 171
- Experimental Design StatisticsComprehensive workflow for statistical experimental design in genomics, from power analysis and sample size determination to batch-balanced experimental layouts and multiple testing strategy.Votes: 0GitHub stars: 171
- Functional Enrichment From DegsTranslate differential expression results into biological insights using GSEA and ORA.Votes: 0GitHub stars: 171
- Genetic Variant AnnotationAnnotate genomic variants in VCF files with functional effects, clinical significance, and pathogenicity predictions.Votes: 0GitHub stars: 171
- Grn PyscenicInfer gene regulatory networks (GRNs) de novo from single-cell RNA-seq data using pySCENIC. This workflow discovers transcription factor (TF) regulons directly from expression patterns and calculates cell-level TF activity scores.Votes: 0GitHub stars: 171
- Gwas To Function TwasIdentify genes whose genetically regulated expression is associated with disease risk, determine therapeutic directionality (inhibit vs. activate), and prioritize drug targets with causal genetic evidence using Transcriptome-Wide Association Study (TWAS) analysis. **Key capabilities:** - Dual TWAS tools: FUSION (comprehensive) and S-PrediXcan (fast, 10-100x faster) - Therapeutic directionality: Determine inhibit vs. activate strategy for each gene - Multi-tier analysis: Basic → Colocalization...Votes: 0GitHub stars: 171
- Lasso Biomarker PanelSelect minimal, interpretable biomarker panels from high-dimensional omics data using penalized logistic regression (LASSO/elastic net) with nested cross-validation and stability selection.Votes: 0GitHub stars: 171
- Literature PreclinicalSearch Consensus (consensus.app) for preclinical studies on a molecular target in a disease, then extract structured in vitro and in vivo experiment details from each paper. ---Votes: 0GitHub stars: 171
- Mendelian Randomization Twosamplemr- You have **GWAS summary statistics** for an exposure and outcome trait - You want to test **causal direction** between two traits (not just correlation) - You need to assess whether an observed association is likely causal or confounded - You want to use **genetic variants as instrumental variables** (natural experiment) - You have OpenGWAS trait IDs **or** your own GWAS summary statistics filesVotes: 0GitHub stars: 171
- Multi Omics IntegrationIdentify **latent factors** driving variation across 2+ omics layers using **MOFA+** (Multi-Omics Factor Analysis). Decomposes multi-omics data into interpretable factors, each capturing shared or view-specific biological signal. Handles **missing data** across views natively.Votes: 0GitHub stars: 171
- Pcr Primer DesignComprehensive PCR and qPCR primer design following MIQE 2.0 guidelines with automated validation.Votes: 0GitHub stars: 171
- Polygenic Risk Score Prs Catalog- **You have a trait of interest** and want to calculate PRS using published, peer-reviewed weights - **Multi-trait risk profiling** (e.g., cardiometabolic panel: CAD, T2D, LDL, BMI, blood pressure) - **Population comparisons** of genetic risk across ancestry groups - **No GWAS summary statistics needed** — uses pre-computed weights from PGS Catalog - **Quick PRS** — minutes per trait (download + score), no LD computation requiredVotes: 0GitHub stars: 171
- Pooled Crispr ScreensAnalyze pooled CRISPR screens with single-cell RNA-seq readout using a tiered workflow: fast screening → target validation → rigorous differential expression.Votes: 0GitHub stars: 171
- Proteomics Diff ExpDifferential protein expression analysis for TMT/LFQ mass spectrometry proteomics data using limma linear models with DEqMS PSM-count-aware variance correction.Votes: 0GitHub stars: 171
- Scrna Trajectory Inference**Use when you have preprocessed scRNA-seq data and want to:** - ✅ Order cells along a differentiation or disease trajectory (pseudotime) - ✅ Identify branching points and terminal cell fates - ✅ Discover genes driving cell state transitions - ✅ Visualize RNA velocity (direction of cell state change) - ✅ Compute cell fate probabilities with CellRank - ✅ Chain from `scrnaseq-scanpy-core-analysis` outputVotes: 0GitHub stars: 171
- Scrnaseq Scanpy Core AnalysisComplete workflow for single-cell RNA-seq analysis using Scanpy and the scverse ecosystem. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.Votes: 0GitHub stars: 171
- Scrnaseq Seurat Core AnalysisComplete workflow for single-cell RNA-seq analysis using Seurat v5. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.Votes: 0GitHub stars: 171
- Spatial Transcriptomics- You have **10x Visium** spatial gene expression data (with or without H&E image) - You want to identify **spatially variable genes** across a tissue section - You want to discover **spatial tissue domains** via clustering - You want to quantify **neighborhood enrichment** between cell clusters - You want to analyze **co-occurrence** patterns of cell types across distances - Input is Space Ranger output, `.h5ad`, or `.h5` fileVotes: 0GitHub stars: 171
- Survival Analysis ClinicalKaplan-Meier survival estimation, Cox proportional hazards regression, and risk stratification for clinical and real-world evidence (RWE) datasets.Votes: 0GitHub stars: 171
- Upstream Regulator AnalysisIdentify transcription factors (TFs) driving observed differential expression by integrating **ChIP-Atlas TF binding data** (epigenomics) with **RNA-seq DE results** (transcriptomics). Ranks TFs by a combined regulatory score incorporating binding enrichment, target-DE overlap (Fisher's exact test), and directional concordance (activator vs repressor).Votes: 0GitHub stars: 171
- Clinical Interpretation--> --- name: bio-variant-calling-clinical-interpretation description: Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants. tool_type: mixed primary_tool: InterVar measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Prioritize and interpret vari...Votes: 0GitHub stars: 171
- Consensus Sequences--> --- name: bio-consensus-sequences description: Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes. tool_type: cli primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Apply variants to reference FASTA using bcftools consensus.Votes: 0GitHub stars: 171
- Deepvariant--> --- name: bio-variant-calling-deepvariant description: Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller. tool_type: cli primary_tool: DeepVariant measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 171
- Filtering Best Practices--> --- name: bio-variant-calling-filtering-best-practices description: Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices. tool_type: mixed primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 171
- Gatk Variant Calling--> --- name: bio-gatk-variant-calling description: Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller. tool_type: cli primary_tool: gatk measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- GATK HaplotypeCall...Votes: 0GitHub stars: 171
- Joint Calling--> --- name: bio-variant-calling-joint-calling description: Joint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples. tool_type: cli primary_tool: GATK measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Call variants jointly across multi...Votes: 0GitHub stars: 171
- Structural Variant Calling--> --- name: bio-variant-calling-structural-variant-calling description: Call structural variants (SVs) from short-read sequencing using Manta, Delly, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations that are too large for standard SNV callers. Use when detecting structural variants from short-read data. tool_type: cli primary_tool: manta measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_fi...Votes: 0GitHub stars: 171
- Tooluniverse Systems BiologyComprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.Votes: 0GitHub stars: 171
- Tooluniverse Target ResearchGather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.Votes: 0GitHub stars: 171
- Tooluniverse Variant AnalysisProduction-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV c...Votes: 0GitHub stars: 171
- Tooluniverse Variant InterpretationSystematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actiona...Votes: 0GitHub stars: 171
- Tpd Ternary Complex Agent--> --- name: 'tpd-ternary-complex-agent' description: 'AI-powered ternary complex prediction for targeted protein degradation, modeling POI-degrader-E3 ligase assemblies to optimize PROTAC and molecular glue efficacy.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **TPD Ternary Complex Agent** specializes in predicting and modeling ternary complex formation for targeted protein degradation (...Votes: 0GitHub stars: 171
- Trial Eligibility Agent--> --- name: trial-eligibility-agent description: Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 171
- Trialgpt Matching--> --- name: trialgpt-matching description: Trial shortlist keywords: - retrieval - ranking - ClinicalTrials - patient-profile measurable_outcome: Produce ≥5 ranked trials (when available) with rationale + missing-data notes within 3 minutes of receiving a patient query. license: MIT metadata: author: TrialGPT Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- Run the locally checked-out TrialGPT pipeline to retrieve, rank, and expla...Votes: 0GitHub stars: 171
- Tumor Clonal Evolution Agent--> --- name: 'tumor-clonal-evolution-agent' description: 'AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Tumor Clonal Evolution Agent** analyzes intratumoral heterogeneity (ITH), reconstructs tumor phylogenies, and tracks clon...Votes: 0GitHub stars: 171
- Tumor Heterogeneity Agent--> --- name: 'tumor-heterogeneity-agent' description: 'AI-powered intratumor heterogeneity analysis for clonal architecture reconstruction, subclonal evolution tracking, and therapy resistance prediction using multi-region and longitudinal sequencing.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Tumor Heterogeneity Agent** provides comprehensive analysis of intratumor heterogeneity (ITH)...Votes: 0GitHub stars: 171
- Tumor Mutational Burden Agent--> --- name: 'tumor-mutational-burden-agent' description: 'Calculates and harmonizes Tumor Mutational Burden (TMB) across platforms to predict immunotherapy response.' keywords: - tmb - immunotherapy - biomarker - harmonization - oncology measurable_outcome: 'Harmonizes TMB scores across 5+ assay platforms with <5% variance from WES gold standard.' allowed-tools: - read_file - run_shell_command --- The **Tumor Mutational Burden Agent** provides comprehensive TMB analysis for immunotherapy re...Votes: 0GitHub stars: 171
- Universal Single Cell Annotator--> --- name: 'universal-single-cell-annotator' description: 'Annotate scRNA-seq' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context.Votes: 0GitHub stars: 171
- UsmlePrepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning.Votes: 0GitHub stars: 171
- VarCADD--> --- name: varcadd-pathogenicity description: Variant Scorer keywords: - variant-interpretation - CADD - pathogenicity - genomics - prediction measurable_outcome: Return pathogenicity scores for a VCF of 1000 variants within 2 minutes, flagging top 1% deleterious hits. license: Non-Commercial metadata: author: Genome Medicine 2025 version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- Genome-wide pathogenicity prediction leveraging standing...Votes: 0GitHub stars: 171
- Variant Annotation--> --- name: bio-variant-annotation description: Comprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance. Use when annotating variants with functional and clinical information. tool_type: mixed primary_tool: VEP measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---Votes: 0GitHub stars: 171
- Variant Calling--> --- name: bio-variant-calling description: Call SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments. tool_type: cli primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Call SNPs and indels from aligned reads using bcftools.Votes: 0GitHub stars: 171
- Variant Interpretation Acmg--> --- name: 'variant-interpretation-acmg' description: 'Classifies genetic variants according to ACMG (American College of Medical Genetics) guidelines.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Variant Interpretation Skill** automates the classification of genetic variants (Pathogenic, Benign, VUS) using a rules-based engine derived from ACMG guidelines.Votes: 0GitHub stars: 171
- Variant Normalization--> --- name: bio-variant-normalization description: Normalize indel representation and split multiallelic variants using bcftools norm. Use when comparing variants from different callers or preparing VCF for downstream analysis. tool_type: cli primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Left-align indels and split multiallelic sites using bcftools norm.Votes: 0GitHub stars: 171
- Vcf Basics--> --- name: bio-vcf-basics description: View, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure. tool_type: cli primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- View and query variant files using bcftools and cyvcf2.Votes: 0GitHub stars: 171
- Vcf Manipulation--> --- name: bio-vcf-manipulation description: Merge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data. tool_type: cli primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Merge, concat, sort, and compare VCF files using bcftools.Votes: 0GitHub stars: 171
- Vcf Statistics--> --- name: bio-vcf-statistics description: Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents. tool_type: cli primary_tool: bcftools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Generate statistics and quality metrics using bcftools.Votes: 0GitHub stars: 171
- Virtual Lab Agent--> --- name: 'virtual-lab-agent' description: 'AI-powered virtual laboratory orchestrating multi-agent scientific research teams for autonomous hypothesis generation, experimental design, and validation in biomedical research.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Virtual Lab Agent** orchestrates AI-powered virtual scientific research teams consisting of specialized agents (Princi...Votes: 0GitHub stars: 171