Claude Skills by FreedomIntelligence

Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.

--> --- name: bio-splicing-pipeline description: End-to-end alternative splicing analysis from FASTQ to differential splicing results. Aligns with STAR 2-pass mode, performs junction QC, runs rMATS-turbo for differential analysis, and generates sashimi visualizations. Use when performing comprehensive splicing analysis from raw RNA-seq data. tool_type: mixed primary_tool: rMATS-turbo measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...

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.

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.

--> --- name: bio-sra-data description: Download sequencing data from NCBI SRA using the SRA toolkit. Use when downloading FASTQ files from SRA accessions, prefetching large datasets, or validating SRA downloads. tool_type: cli primary_tool: sra-tools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Download raw sequencing data from the Sequence Read Archive using the SRA toolkit.

Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.

Searches molecular libraries for substructure matches using SMARTS patterns with RDKit. Filters compounds by pharmacophore features, functional groups, or scaffold matches with atom mapping. Use when finding compounds containing specific chemical moieties or filtering libraries by structural features.

--> --- name: bio-systems-biology-context-specific-models description: Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA. tool_type: python primary_tool: cobrapy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...

--> --- name: bio-systems-biology-flux-balance-analysis description: Perform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions. tool_type: python primary_tool: cobrapy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file...

--> --- name: bio-systems-biology-gene-essentiality description: Perform in silico gene knockout analysis and synthetic lethality screens using COBRApy single and double deletions. Predict essential genes and identify synthetic lethal pairs for drug target discovery. Use when identifying essential genes or finding synthetic lethal drug targets. tool_type: python primary_tool: cobrapy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...

--> --- name: bio-systems-biology-metabolic-reconstruction description: Build genome-scale metabolic models from genome sequences using CarveMe and gapseq for automated reconstruction. Generate draft models ready for curation and analysis. Use when creating metabolic models for organisms without existing models. tool_type: cli primary_tool: CarveMe measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-systems-biology-model-curation description: Validate, gap-fill, and curate genome-scale metabolic models using memote for quality scores and COBRApy for manual curation. Ensure models meet SBML standards and produce biologically meaningful predictions. Use when improving draft models or preparing models for publication. tool_type: python primary_tool: memote measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_fil...

Analyze BCR repertoires for somatic hypermutation, clonal lineages, and B cell phylogenetics using the Immcantation framework. Use when studying B cell affinity maturation, germinal center dynamics, or antibody evolution.

Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.

Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.

Analyze single-cell TCR and BCR data integrated with gene expression using scirpy. Use when working with 10x Genomics VDJ data alongside scRNA-seq or when integrating immune receptor information with cell state analysis.

Calculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.

--> --- name: bio-transcription-translation description: Transcribe DNA to RNA and translate to protein using Biopython. Use when converting between DNA, RNA, and protein sequences, finding ORFs, or using alternative codon tables. tool_type: python primary_tool: Bio.Seq measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Convert between DNA, RNA, and protein sequences using Biopython.

Estimates circulating tumor DNA fraction from shallow whole-genome sequencing using ichorCNA. Detects copy number alterations via HMM segmentation and calculates ctDNA percentage. Requires 0.1-1x sWGS coverage. Use when quantifying tumor burden from liquid biopsy or monitoring treatment response.

--> --- name: bio-uniprot-access description: Access UniProt protein database for sequences, annotations, and functional information. Use when retrieving protein data, GO terms, domain annotations, or protein-protein interactions. tool_type: python primary_tool: requests measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Query UniProt for protein sequences, functional annotations, and cross-references.

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.

Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.

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.

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.

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.

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.

Call SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.

Normalize indel representation and split multiallelic variants using bcftools norm. Use when comparing variants from different callers or preparing VCF for downstream analysis.

View, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.

Merge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.

Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.

Performs structure-based virtual screening using AutoDock Vina 1.2 for molecular docking. Prepares receptor PDBQT files, generates ligand conformers, defines binding site boxes, and ranks compounds by predicted binding affinity. Use when screening chemical libraries against a protein structure to find potential binders.

--> --- 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...

--> --- 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...

--> --- 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...

--> --- 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...

--> --- 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...

--> --- 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 - ...

--> --- 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...

--> --- 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 ---

--> --- 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" ...

--> --- 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...

--> --- 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-...

--> --- 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...

--> --- 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...

--> --- name: bio-workflows-fastq-to-variants description: End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads. tool_type: cli primary_tool: bcftools workflow: true depends_on: - read-qc/fastp-workflow - read-alignment/bwa-alignment - alignment-files/alignment-sorting - alignment-files/duplicate-handling - variant-calli...

--> --- name: bio-workflows-genome-assembly-pipeline description: End-to-end genome assembly workflow from reads to polished assembly with QC. Supports short reads (SPAdes), long reads (Flye), and hybrid approaches. Use when assembling genomes from raw reads. tool_type: cli primary_tool: Flye workflow: true depends_on: - read-qc/fastp-workflow - genome-assembly/short-read-assembly - genome-assembly/long-read-assembly - genome-assembly/assembly-polishing - genome-assembly/assembly-qc qc_checkp...

--> --- name: bio-workflows-gwas-pipeline description: End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies. tool_type: mixed primary_tool: PLINK2 workflow: true depends_on: - population-genetics/plink-basics - population-genetics/population-structure - population-genetics/association-testing - population-genetics/linkage-dise...

--> --- name: bio-workflows-hic-pipeline description: End-to-end Hi-C analysis workflow from contact pairs to compartments, TADs, and loops. Covers cooler matrices, cooltools analysis, and visualization. Use when processing Hi-C data to compartments and TADs. tool_type: mixed primary_tool: cooler workflow: true depends_on: - hi-c-analysis/hic-data-io - hi-c-analysis/contact-pairs - hi-c-analysis/matrix-operations - hi-c-analysis/compartment-analysis - hi-c-analysis/tad-detection - hi-c-analys...