Claude Skills by FreedomIntelligence

Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.

Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or point mutations.

--> --- name: bio-genome-intervals-bed-file-basics description: BED file format fundamentals, creation, validation, and basic operations. Covers BED3 through BED12 formats, coordinate systems, sorting, and format conversion using bedtools and pybedtools. Use when working with genomic coordinates or preparing interval files for downstream tools. tool_type: mixed primary_tool: bedtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...

--> --- name: bio-genome-intervals-bigwig-tracks description: Create and read bigWig browser tracks for visualizing continuous genomic data. Convert bedGraph to bigWig, extract signal values, and generate coverage tracks using UCSC tools and pyBigWig. Use when preparing coverage tracks for genome browsers or extracting signal at specific regions. tool_type: mixed primary_tool: pyBigWig measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: ...

--> --- name: bio-genome-intervals-coverage-analysis description: Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency. tool_type: mixed primary_tool: bedtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tool...

--> --- name: bio-genome-intervals-gtf-gff-handling description: Parse, query, and convert GTF and GFF3 annotation files. Extract gene, transcript, and exon coordinates using gffread, gtfparse, and gffutils. Use when extracting specific features from gene annotations or converting between annotation formats. tool_type: mixed primary_tool: gffread measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- GTF ...

--> --- name: bio-genome-intervals-interval-arithmetic description: Core interval arithmetic operations including intersect, subtract, merge, complement, map, and groupby using bedtools and pybedtools. Use when finding overlapping regions, removing overlaps, combining adjacent intervals, or transferring annotations between interval files. tool_type: mixed primary_tool: bedtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_f...

--> --- name: bio-genome-intervals-proximity-operations description: Find nearest features, search within windows, and extend intervals using closest, window, flank, and slop operations. Use when performing TSS proximity analysis, assigning enhancers to genes, defining promoter regions, or finding nearby genomic features. tool_type: mixed primary_tool: bedtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_c...

--> --- name: bio-geo-data description: Query NCBI Gene Expression Omnibus (GEO) for expression datasets using Biopython Bio.Entrez. Use when finding microarray/RNA-seq datasets, downloading expression data, or linking GEO series to SRA runs. tool_type: python primary_tool: Bio.Entrez measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Query and access Gene Expression Omnibus datasets using Biopython's...

Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.

Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.

Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.

Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.

Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.

Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization. Use when starting IMC analysis from raw MCD files or preparing images for segmentation.

Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.

Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in multiplexed imaging data.

Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.

Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.

Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens. Use when designing vaccines, mapping antibody binding sites, or predicting immunogenic peptides.

Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.

Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.

Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses. Use when identifying vaccine targets or checkpoint inhibitor response biomarkers from tumor sequencing data.

Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition. Match TCRs to their cognate epitopes or predict TCR targets. Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.

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.

--> --- name: bio-liquid-biopsy-pipeline description: Cell-free DNA analysis pipeline from plasma sequencing to tumor monitoring. Preprocesses cfDNA reads, analyzes fragment patterns, estimates tumor fraction from sWGS, and optionally detects mutations from targeted panels. Use when analyzing liquid biopsy samples for cancer detection or monitoring. tool_type: mixed primary_tool: ichorCNA measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tool...

--> --- name: bio-local-blast description: Run local BLAST searches using BLAST+ command-line tools. Use when running fast unlimited searches, building custom databases, performing large-scale analysis, or when NCBI servers are slow or unavailable. tool_type: cli primary_tool: BLAST+ measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Run BLAST searches locally using NCBI BLAST+ command-line tools.

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

Analyze PacBio Iso-Seq data for full-length isoform discovery and quantification. Use when characterizing transcript diversity or identifying novel splice variants.

Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.

Tracks ctDNA dynamics over time for treatment response monitoring using serial liquid biopsy samples. Analyzes tumor fraction trends, mutation clearance kinetics, and defines molecular response criteria. Use when monitoring patients during therapy or detecting molecular relapse before clinical progression.

Align long reads using minimap2 for Oxford Nanopore and PacBio data. Supports various presets for different read types and applications. Use when aligning ONT or PacBio reads to a reference genome for variant calling, SV detection, or coverage analysis.

Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.

Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.

Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.

--> --- name: bio-machine-learning-atlas-mapping description: Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models. tool_type: python primary_tool: scvi-tools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_comm...

--> --- name: bio-machine-learning-biomarker-discovery description: Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data. tool_type: python primary_tool: boruta measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-machine-learning-model-validation description: Implements nested cross-validation and stratified splits for unbiased model evaluation on biomedical datasets. Prevents data leakage and overfitting in biomarker discovery. Use when validating classifiers or optimizing hyperparameters on omics data. tool_type: python primary_tool: sklearn measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-machine-learning-omics-classifiers description: Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data. tool_type: python primary_tool: sklearn measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. all...

--> --- name: bio-machine-learning-prediction-explanation description: Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions. tool_type: python primary_tool: shap measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

Local protein structure prediction with AlphaFold, Boltz, or Chai. Compare predicted structures, compute RMSD, visualise 3D models.

--> --- name: 'tcell-exhaustion-analysis-agent' description: 'AI-powered analysis of T-cell exhaustion states, epigenetic scarring, stem-like T-cell populations, and checkpoint blockade response prediction in cancer immunotherapy.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **T-Cell Exhaustion Analysis Agent** provides comprehensive profiling of T-cell dysfunction states in cancer and chro...

Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.

--> --- name: 'tcr-pmhc-prediction-agent' description: 'AI-powered TCR-peptide-MHC interaction prediction using AlphaFold3 and deep learning for therapeutic TCR discovery, neoantigen validation, and T cell immunogenicity assessment.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **TCR-pMHC Prediction Agent** predicts T-cell receptor interactions with peptide-MHC complexes using AlphaFold3-bas...

--> --- name: 'tcr-repertoire-analysis-agent' description: 'AI-powered T-cell receptor repertoire analysis for cancer diagnosis, immunotherapy response prediction, and therapeutic TCR selection using deep learning and multi-layer ML approaches.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **TCR Repertoire Analysis Agent** provides comprehensive T-cell receptor repertoire analysis for cancer...

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.