Skill Claude CodeCodex
Load when matching LC-MS m/z features to an explicit local metabolite reference within a ppm tolerance; bundled HMDB entries are for explicit demonstrations only. Skip pathway ORA (use metabolomics-pathway-enrichment) and spectral matching or online searches (use external SIRIUS / GNPS).
Skill Claude CodeCodex
Load 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); raw spectra.
Skill Claude CodeCodex
Load 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); raw spectra (use metabolomics-xcms-preprocessing).
Skill Claude CodeCodex
Load when running metabolite-name ORA against an explicit local pathway reference with BH-FDR; bundled pathway sets are for explicit demonstrations only. Skip m/z annotation (use metabolomics-annotation) and topology analysis or online pathway retrieval (use external mummichog / FELLA or database tools).
Skill Claude CodeCodex
Load when running per-sample peak picking on a feature × intensity table via scipy.signal.findpeaks — emits per-(sample, feature) detected peaks with prominence and width. Skip when working with mz / RT raw scans (use metabolomics-xcms-preprocessing); only normalising / quantifying (use metabolomics-quantification).
Skill Claude CodeCodex
Load 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); the input is raw spectra (use metabolomics-xcms-preprocessing).
Skill Claude CodeCodex
Load 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 (use metabolomics-xcms-preprocessing); two-group DE with default ctrl / treat…
Skill Claude CodeCodex
Load when exercising the CLI and replay pipeline with a synthetic LC-MS peak table. Skip real mzML preprocessing (run XCMS externally); table peak picking belongs to metabolomics-peak-detection.
Skill Claude CodeCodex
Load when ingesting a MaxQuant proteinGroups.txt, FragPipe combinedprotein.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); the file is already OmicsClaw schema.
Skill Claude CodeCodex
Load 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); label-based TMT linear-mixed models.
Skill Claude CodeCodex
Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways. Skip rank-based GSEA (use bulkrna-enrichment) or protein differential testing (use proteomics-de).
Skill Claude CodeCodex
Load 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); working with protein-quantification tables (use…
Skill Claude CodeCodex
Load 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); peptide-level QC is needed (use proteomics-identification).
Skill Claude CodeCodex
Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by localizationprobability), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input; you only need protein-level…
Skill Claude CodeCodex
Load 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); label-based TMT / iTRAQ workflows (search upstream first).
Skill Claude CodeCodex
Load 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); no XL-MS experiment was…
Skill Claude CodeCodex
Load 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; BAM (peak calling not implemented here); scRNA preprocessing (use sc-preprocessing).
Skill Claude CodeCodex
Load 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); before counts exist (use sc-count).
Skill Claude CodeCodex
Load 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); upstream merging only (use sc-multi-count).
Skill Claude CodeCodex
Load 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); condition-vs-control DE (use sc-de).
Skill Claude CodeCodex
Load 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); transcription factor → target regulatory networks (use sc-grn).
Skill Claude CodeCodex
Load 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); marker ranking after clustering (use sc-markers).
Skill Claude CodeCodex
Load 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); raw quality assessment only (use sc-fastq-qc).
Skill Claude CodeCodex
Load 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); marker-based cell-type labelling (use sc-cell-annotation).
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: