All authors
Claude Skills by FreedomIntelligence
github.com/FreedomIntelligence741 skills2 installs1,922 views
- Bio Restriction Sites--> --- name: bio-restriction-sites description: Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Search with single enzymes, batches of enzymes, or commercially available enzyme sets. Returns cut positions for linear or circular DNA. Use when finding restriction enzyme cut sites in sequences. tool_type: python primary_tool: Bio.Restriction measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file...Votes: 0GitHub stars: 2,984
- Bio Reverse Complement--> --- name: bio-reverse-complement description: Generate reverse complements and complements of DNA/RNA sequences using Biopython. Use when working with opposite strands, primer design, or converting between template and coding strands. 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 --- Generate complementary and reverse complementary sequences using Biopython.Votes: 0GitHub stars: 2,984
- Bio Ribo Seq Orf DetectionDetect 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.Votes: 0GitHub stars: 2,984
- Bio Ribo Seq Riboseq PreprocessingPreprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation.Votes: 0GitHub stars: 2,984
- Bio Ribo Seq Ribosome PeriodicityValidate 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.Votes: 0GitHub stars: 2,984
- Bio Ribo Seq Ribosome StallingDetect 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.Votes: 0GitHub stars: 2,984
- Bio Ribo Seq Translation EfficiencyCalculate 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.Votes: 0GitHub stars: 2,984
- Bio Rna Quantification 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: 2,984
- Bio Rna Quantification 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: 2,984
- Bio Rna Quantification 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: 2,984
- Bio Rna Quantification 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: 2,984
- Bio Rnaseq QcRNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.Votes: 0GitHub stars: 2,984
- Bio Sam Bam Basics--> --- name: bio-sam-bam-basics description: View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure. tool_type: cli primary_tool: samtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- View and convert alignment files using samtools and pysam.Votes: 0GitHub stars: 2,984
- Bio Sashimi PlotsCreates 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.Votes: 0GitHub stars: 2,984
- Bio Seq Objects--> --- name: bio-seq-objects description: Create and manipulate Seq, MutableSeq, and SeqRecord objects using Biopython. Use when creating sequences from strings, modifying sequence data in-place, or building annotated sequence records. 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 --- Create and manipulate biological sequence objects using Biopython.Votes: 0GitHub stars: 2,984
- Bio Sequence Properties--> --- name: bio-sequence-properties description: Calculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences. tool_type: python primary_tool: Bio.SeqUtils measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Calculate physical and chemical properties of bi...Votes: 0GitHub stars: 2,984
- Bio Sequence Similarity--> --- name: bio-sequence-similarity description: Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough. tool_type: mixed primary_tool: BLAST+ measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Advanced methods for find...Votes: 0GitHub stars: 2,984
- Bio Sequence Slicing--> --- name: bio-sequence-slicing description: Slice, extract, and concatenate biological sequences using Biopython. Use when extracting subsequences, joining sequences, or manipulating sequence regions by position. 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 --- Extract, slice, and concatenate sequences using Biopython's Seq objects.Votes: 0GitHub stars: 2,984
- Bio Sequence StatisticsCalculate sequence statistics (N50, length distribution, GC content, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing assemblies.Votes: 0GitHub stars: 2,984
- Bio Similarity SearchingPerforms molecular similarity searches using Tanimoto coefficient on fingerprints via RDKit. Finds structurally similar compounds using ECFP or MACCS keys and clusters molecules by structural similarity using Butina clustering. Use when finding analogs of a query compound or clustering chemical libraries.Votes: 0GitHub stars: 2,984
- Bio Single Cell Batch IntegrationIntegrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.Votes: 0GitHub stars: 2,984
- Bio Single Cell Cell AnnotationAutomated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.Votes: 0GitHub stars: 2,984
- Bio Single Cell Cell CommunicationInfer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis. Use when inferring ligand-receptor interactions between cell types.Votes: 0GitHub stars: 2,984
- Bio Single Cell ClusteringDimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell data.Votes: 0GitHub stars: 2,984
- Bio Single Cell Data IoRead, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.Votes: 0GitHub stars: 2,984
- Bio Single Cell Doublet DetectionDetect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.Votes: 0GitHub stars: 2,984
- Bio Single Cell Lineage TracingReconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories.Votes: 0GitHub stars: 2,984
- Bio Single Cell Markers AnnotationFind marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.Votes: 0GitHub stars: 2,984
- Bio Single Cell Metabolite CommunicationAnalyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.Votes: 0GitHub stars: 2,984
- Bio Single Cell Multimodal IntegrationAnalyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.Votes: 0GitHub stars: 2,984
- Bio Single Cell Perturb SeqAnalyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells.Votes: 0GitHub stars: 2,984
- Bio Single Cell PreprocessingQuality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.Votes: 0GitHub stars: 2,984
- Bio Single Cell Scatac AnalysisSingle-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.Votes: 0GitHub stars: 2,984
- Bio Single Cell SplicingAnalyzes 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.Votes: 0GitHub stars: 2,984
- Bio Single Cell Trajectory InferenceInfer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime.Votes: 0GitHub stars: 2,984
- Bio Small Rna Seq 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: 2,984
- Bio Small Rna Seq 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: 2,984
- Bio Small Rna Seq 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: 2,984
- Bio Small Rna Seq 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: 2,984
- Bio Small Rna Seq 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: 2,984
- Bio Spatial Transcriptomics Image AnalysisProcess and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial CommunicationAnalyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial Data IoLoad spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial DeconvolutionEstimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial DomainsIdentify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial MultiomicsAnalyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial NeighborsBuild spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial PreprocessingQuality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial ProteomicsAnalyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.Votes: 0GitHub stars: 2,984
- Bio Spatial Transcriptomics Spatial StatisticsCompute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.Votes: 0GitHub stars: 2,984