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Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,578 skills3 installs3,530 views
- Bulkrna SurvivalLoad when stratifying patients by gene expression and testing for survival differences (Kaplan-Meier + Cox) in bulk RNA-seq. Skip if no time-to-event clinical data exists, or for non-bulk cohorts (single-cell / spatial survival is not supported).Votes: 0GitHub stars: 32
- Bulkrna TrajblendLoad when placing bulk RNA-seq samples on a single-cell reference's pseudotime axis (NNLS deconvolution + nearest-neighbour mapping). Skip for plain cell-type proportions (use bulkrna-deconvolution alone) or for native single-cell trajectory inference (use sc-pseudotime).Votes: 0GitHub stars: 32
- Genomics AlignmentLoad when computing alignment QC metrics (mapping rate, MAPQ distribution, insert size, duplicate rate, proper-pair rate) from a SAM or BAM file produced by any short-/long-read aligner (BWA / Bowtie2 / Minimap2). Skip when running the alignment step itself or when only FASTQ-level QC is needed (use `genomics-qc`).Votes: 0GitHub stars: 32
- Genomics AssemblyLoad when computing genome-assembly QC metrics — N50/N90, L50/L90, total length, contig count, GC content, longest-contig — from a FASTA produced by any assembler (SPAdes / Megahit / Flye / Canu). Skip when running the assembly itself or when assessing alignment quality (use `genomics-alignment`).Votes: 0GitHub stars: 32
- Genomics Cnv CallingLoad when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (`amplification` / `gain` / `neutral` / `loss` / `deep_deletion`), per-chromosome summary, genome-fraction-altered. Skip when working with single-cell / spatial CNV (use `spatial-cnv`).Votes: 0GitHub stars: 32
- Genomics EpigenomicsLoad when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first) or when working with single-cell ATAC (use `scatac-preprocessing`).Votes: 0GitHub stars: 32
- Genomics PhasingLoad when summarising a phased VCF (output of WhatsHap / SHAPEIT5 / Eagle2) — phased fraction of het variants, phase-block N50, PS-field parsing, pipe-delimited genotype detection. Skip when the input is unphased (run a phaser first) or when calling small variants (use `genomics-variant-calling`).Votes: 0GitHub stars: 32
- Genomics QcLoad when running pre-alignment FASTQ quality control — Phred quality scores, Q20/Q30 rates, GC / N content, read-length distribution, adapter-contamination detection. Skip when working with already-aligned BAMs (use `genomics-alignment`) or when peak / variant files are the input (use the relevant downstream skill).Votes: 0GitHub stars: 32
- Genomics Sv DetectionLoad when summarising structural variants from an SV VCF (DEL / DUP / INV / TRA) — BND-notation parsing, size classification, per-type counts. Skip when working with small SNVs / indels (use `genomics-variant-calling`) or calling SVs from BAM (run Manta / Delly / Sniffles first).Votes: 0GitHub stars: 32
- Genomics Variant AnnotationLoad when summarising functional impact of an annotated variant CSV — per-IMPACT counts (HIGH / MODERATE / LOW / MODIFIER), top consequences, gene-affected count. Skip when input is a raw VCF (convert with `bcftools +split-vep` first), when calling raw variants (use `genomics-variant-calling`), or filtering VCFs (use `genomics-vcf-operations`).Votes: 0GitHub stars: 32
- Genomics Variant CallingLoad when summarising small variants (SNVs / indels) from a VCF or computing demo-pattern variant statistics (Ti/Tv ratio, per-chromosome distribution, SNP / indel split). Skip when filtering / merging VCFs (use `genomics-vcf-operations`), when calling structural variants (use `genomics-sv-detection`), or when adding functional annotations (use `genomics-variant-annotation`).Votes: 0GitHub stars: 32
- Genomics Vcf OperationsLoad when summarising / filtering a VCF — variant classification (SNP / MNP / INS / DEL / COMPLEX), Ti/Tv ratio, QUAL / DP threshold filtering, INFO-field parsing. Skip when the input is a BAM (use `genomics-variant-calling` upstream first) or when adding functional annotations (use `genomics-variant-annotation`).Votes: 0GitHub stars: 32
- LiteratureLoad when extracting GEO accessions, dataset metadata, and downloadable references from a scientific paper (PDF / URL / DOI / PubMed ID / raw text) for downstream omics analysis. Skip when the dataset is already in hand or when only routing a query (use `orchestrator`).Votes: 0GitHub stars: 32
- Metabolomics AnnotationLoad when annotating LC-MS features against a built-in 15-metabolite HMDB demo dictionary by m/z within a `--ppm` tolerance — emits a per-feature annotation table. Skip when needing real HMDB / KEGG / LipidMaps / METLIN look-up (this skill is demo-only) or for raw spectra (use `metabolomics-xcms-preprocessing` first).Votes: 0GitHub stars: 32
- Metabolomics DeLoad when running two-group metabolomics DE (t-test + log2FC + BH-FDR + PCA) on a feature × sample CSV using `--group-a-prefix` / `--group-b-prefix` (default `ctrl` / `treat`). Skip when needing tunable test backends (use `metabolomics-statistics` for Wilcoxon / ANOVA / Kruskal) or for raw spectra.Votes: 0GitHub stars: 32
- Metabolomics NormalizationLoad when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table. Skip when also imputing (use `metabolomics-quantification`) or for raw spectra (run `metabolomics-xcms-preprocessing` first).Votes: 0GitHub stars: 32
- Metabolomics Pathway EnrichmentLoad when running over-representation analysis (ORA) on a metabolite list via Fisher's exact test against a built-in 9-pathway DEMO dictionary, BH-FDR adjusted. Skip when needing real KEGG / Reactome (this skill is demo-only) or `mummichog` / `fella` topology methods (CLI accepts them but only ORA runs).Votes: 0GitHub stars: 32
- Metabolomics Peak DetectionLoad when running per-sample peak picking on a feature × intensity table via `scipy.signal.find_peaks` — emits per-(sample, feature) detected peaks with prominence and width. Skip when working with mz / RT raw scans (use `metabolomics-xcms-preprocessing` upstream) or when only normalising / quantifying (use `metabolomics-quantification`).Votes: 0GitHub stars: 32
- Metabolomics QuantificationLoad when imputing missing values (min / median / KNN) and normalising (TIC / median / log) a feature × sample metabolomics CSV. Skip when only normalisation is needed (use `metabolomics-normalization`) or when the input is raw spectra (run `metabolomics-xcms-preprocessing` first).Votes: 0GitHub stars: 32
- Metabolomics StatisticsLoad when running univariate two-group testing (t-test / Wilcoxon / ANOVA / Kruskal-Wallis) on a feature × sample metabolomics CSV with `--group1-prefix` / `--group2-prefix` column matching, BH-FDR adjusted. Skip when working with raw spectra (run `metabolomics-xcms-preprocessing`) or for two-group DE with default `ctrl` / `treat` prefixes (use `metabolomics-de`).Votes: 0GitHub stars: 32
- Metabolomics Xcms PreprocessingLoad when running an XCMS-style preprocessing summary on LC-MS metabolomics raw / vendor-converted files — emits a peak table with m/z, retention time, and per-sample intensities. Skip when working with an already-built peak table (use `metabolomics-peak-detection`) or when only annotation is needed (use `metabolomics-annotation`).Votes: 0GitHub stars: 32
- OrchestratorLoad when routing a natural-language omics query to the correct domain skill across spatial / singlecell / genomics / proteomics / metabolomics / bulkrna domains via keyword / LLM / hybrid matching. Skip when the target skill is already known — invoke that skill directly.Votes: 0GitHub stars: 32
- Omics Skill BuilderLoad when scaffolding a NEW OmicsClaw skill from a natural-language request — generates the skill directory layout (SKILL.md, parameters.yaml, references/, tests/) under the chosen domain. Skip when modifying an existing skill (edit its files directly) or when only routing a query (use `orchestrator`).Votes: 0GitHub stars: 32
- Proteomics Data ImportLoad when ingesting a MaxQuant `proteinGroups.txt`, FragPipe `combined_protein.tsv`, DIA-NN report, or generic CSV / TSV protein-quantification table — normalises columns to a standard schema, emits `tables/proteins.csv`. Skip when raw spectra are the input (run the search engine first) or when the file is already OmicsClaw schema.Votes: 0GitHub stars: 32
- Proteomics DeLoad when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually) or label-based TMT linear-mixed models.Votes: 0GitHub stars: 32
- Proteomics EnrichmentLoad when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only — use `bulkrna-enrichment` for real KEGG / Reactome / MSigDB) or for rank-based GSEA.Votes: 0GitHub stars: 32
- Proteomics IdentificationLoad when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine first) or when working with protein-quantification tables (use `proteomics-ms-qc`).Votes: 0GitHub stars: 32
- Proteomics Ms QcLoad when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first) or when peptide-level QC is needed (use `proteomics-identification`).Votes: 0GitHub stars: 32
- Proteomics PtmLoad when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by `localization_probability`), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input or when you only need protein-level abundance (use `proteomics-quantification`).Votes: 0GitHub stars: 32
- Proteomics QuantificationLoad when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use `proteomics-ms-qc` for QC) or for label-based TMT / iTRAQ workflows (search upstream first).Votes: 0GitHub stars: 32
- Proteomics StructuralLoad when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance. Skip when raw spectra are the input (run XlinkX / pLink / xiSEARCH first) or no XL-MS experiment was performed.Votes: 0GitHub stars: 32
- Scatac PreprocessingLoad when preprocessing a single-cell ATAC peak × cell AnnData via Signac-style TF-IDF + LSI + Leiden, producing a clustered UMAP-ready object. Skip when input is fragments or BAM (peak calling not implemented here) or for scRNA preprocessing (use sc-preprocessing).Votes: 0GitHub stars: 32
- Sc Ambient RemovalLoad when removing ambient RNA contamination from droplet-based scRNA-seq using a simple subtraction path, CellBender, or SoupX. Skip when the contamination is multiplet barcodes (use sc-doublet-detection) or before counts exist (use sc-count).Votes: 0GitHub stars: 32
- Sc Batch IntegrationLoad when integrating multi-sample scRNA-seq with Harmony, scVI, scANVI, BBKNN, Scanorama, SIMBA, or supported R-backed methods to remove batch effects. Skip when the data is one sample (no batch effect to integrate) or for upstream merging only (use sc-multi-count).Votes: 0GitHub stars: 32
- Sc Cell AnnotationLoad when assigning cell-type labels to a clustered scRNA AnnData via marker dictionaries, CellTypist, PopV, KNNPredict, SingleR, scmap, SCSA, or a manual cluster-to-label map. Skip when ranking marker genes per cluster (use sc-markers) or for condition-vs-control DE (use sc-de).Votes: 0GitHub stars: 32
- Sc Cell CommunicationLoad when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R). Skip when assigning cell-type labels (use sc-cell-annotation) or for transcription factor → target regulatory networks (use sc-grn).Votes: 0GitHub stars: 32
- Sc ClusteringLoad when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData. Skip when QC/normalisation/HVG/PCA have not run yet (use sc-preprocessing) or for marker ranking after clustering (use sc-markers).Votes: 0GitHub stars: 32
- Sc Consensus ClusteringMulti-resolution typed consensus over sc-clustering. Fans out leiden / louvain at several resolutions in parallel, scores members by silhouette + cross-method NMI, runs kmode / weighted / LCA consensus on the surviving base clusterings, and emits a verified report carrying the mandatory A-path banner per ADR 0010.Votes: 0GitHub stars: 32
- Sc CountLoad when turning scRNA FASTQ (or existing CellRanger/STARsolo/SimpleAF/kb-python output) into a downstream-ready AnnData. Skip when reads are already counted into AnnData (use sc-standardize-input) or for raw quality assessment only (use sc-fastq-qc).Votes: 0GitHub stars: 32
- Sc CytotraceLoad when computing per-cell differentiation potency / stemness scores from gene-expression complexity on a scRNA AnnData via the CytoTRACE-simple method. Skip when ordering cells along a trajectory (use sc-pseudotime) or for marker-based cell-type labelling (use sc-cell-annotation).Votes: 0GitHub stars: 32
- Sc DeLoad when finding marker genes per cluster or comparing condition expression in single-cell RNA-seq. Skip if the data is bulk (use bulkrna-de) or spatial (use spatial-de), or for cluster-only markers without conditions (use sc-markers).Votes: 0GitHub stars: 32
- Sc Differential AbundanceLoad when testing whether cell-type / cluster proportions or neighbourhood densities differ between conditions in a multi-sample scRNA AnnData via Milo, scCODA, simple proportion screen, or R Monte-Carlo permutation. Skip when ranking marker genes (use sc-markers) or for per-cell DE (use sc-de).Votes: 0GitHub stars: 32
- Sc Doublet DetectionLoad when annotating putative doublets in single-cell RNA-seq using Scrublet, DoubletDetection, DoubletFinder, scDblFinder, or scds. Skip when ambient RNA is the contamination problem (use sc-ambient-removal) or before counts exist (use sc-fastq-qc / sc-count).Votes: 0GitHub stars: 32
- Sc Drug ResponseLoad when scoring drug sensitivity per cluster on an annotated scRNA AnnData via simple-correlation against drug-target signatures or via CaDRReS-Sc pretrained models (GDSC / PRISM). Skip when the AnnData has no cluster labels yet (run sc-clustering first) or for predicting genetic-perturbation effects (use sc-in-silico-perturbation).Votes: 0GitHub stars: 32
- Sc EnrichmentLoad when running bulk-style pathway enrichment (ORA / GSEA / GSEA-R / GSVA-R) on a per-group ranked DE / marker list against a gene-set library. Skip when computing per-cell pathway scores in-place (use sc-pathway-scoring) or for de-novo gene-program discovery (use sc-gene-programs).Votes: 0GitHub stars: 32
- Sc Fastq QcLoad when checking raw single-cell FASTQ read quality (Phred / GC / adapter / length) before counting. Skip when reads are already counted (use sc-qc) or for bulk FASTQ (use bulkrna-read-qc).Votes: 0GitHub stars: 32
- Sc FilterLoad when removing low-quality cells and lowly-detected genes from a single-cell AnnData using QC-derived thresholds or tissue presets. Skip for the full normalize→HVG→PCA→cluster pipeline (use sc-preprocessing) or when reads are still raw FASTQ (use sc-fastq-qc → sc-count first).Votes: 0GitHub stars: 32
- Sc Gene ProgramsLoad when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData. Skip when ranking marker genes per cluster (use sc-markers) or for inferring TF → target regulons (use sc-grn).Votes: 0GitHub stars: 32
- Sc GrnLoad when inferring TF → target gene regulatory networks on a normalised scRNA AnnData via pySCENIC (GRNBoost2 + cisTarget + AUCell) or correlation-based GRN fallback (when arboreto is unavailable, in --demo, or with --allow-simplified-grn). Skip when computing ligand-receptor cell-cell signalling (use sc-cell-communication) or for predicting genetic-KO effects (use sc-in-silico-perturbation).Votes: 0GitHub stars: 32
- Sc In Silico PerturbationLoad when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R). Skip when you have a real Perturb-seq / CRISPR screen (use sc-perturb / sc-perturb-prep) or for predicting drug sensitivity (use sc-drug-response).Votes: 0GitHub stars: 32