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scverse

@scverse source repo

6 published skills

  1. ▌
    Acumen · scverse bundle
    Use for any question or task involving the Python package `acumen` (its CLI or its API) — setting up a benchmark project against a target package, writing or generating benchmark tasks, drafting/improving/hand-editing an agent Skill, running and interpreting skill-vs-baseline benchmark passes, shipping a skill into the target package, diagnosing a run, and choosing what to do next in that loop — open it before answering or running anything, because acumen's defaults, guardrails and correct next step are not guessable from the command names.
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    Decoupler · scverse bundle
    Use for any task involving the decoupler library — inferring biological activity/enrichment scores from omics data (bulk, single-cell, spatial). Triggers on estimating transcription factor (TF) activity, pathway activity, or gene-set enrichment from an AnnData/DataFrame; running ulm, mlm, ora, gsea, gsva, aucell, viper, zscore, waggr, mdt, udt; multi-method consensus; fetching prior knowledge (CollecTRI, DoRothEA, PROGENy, MSigDB hallmarks, OmniPath); pseudobulk and expression filtering; using bundled example datasets (pbmc3k, covid5k, erygast1k, hsctgfb, msvisium, knocktf, toy) and whether they are raw counts or normalized; ranking/plotting activity scores; or benchmarking methods against ground truth. This is a router skill — read the relevant file under references/ before writing decoupler code, because outputs land in different places and several defaults are non-obvious.
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    Liana · scverse bundle
    Cell-cell communication (CCC) inference with the liana Python package (LIANA+, scverse). Use for any task involving liana or ligand-receptor (LR) analysis of AnnData/MuData objects. Triggers on steady-state LR scoring (rank_aggregate, CellPhoneDB, CellChat, NATMI, Connectome, SingleCellSignalR, logFC, scSeqComm); multi-sample or differential CCC (by_sample, MOFA+, Tensor-cell2cell, df_to_lr, pyCrossTalkeR); spatial CCC on Visium, Xenium, MERFISH, CosMx or slide-seq (spatial_neighbors, bivariate local/global metrics, Moran's R, Inflow, LRIC, cross-PCF, MISTy); multimodal CITE-seq or spatial metabolomics; metabolite-mediated CCC via MetalinksDB; LR resources and orthology for mouse or other organisms (consensus, mouseconsensus, OmniPath, HCOP); liana plots (dotplot, tileplot, circle). Also use when the user says cell-cell interactions, crosstalk, signalling between cell types, sender and receiver, or niche signalling, even without naming liana.
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    Pyspy · scverse
    Profile the execution time of a Python script using py-spy and visualize the result with speedscope. Use when the user wants to benchmark performance, find slow code paths, or profile CPU time.
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    Memray · scverse
    Profile the memory usage of a Python script using memray and visualize a temporal flamegraph in the browser. Use when the user wants to investigate memory consumption, find leaks, or understand allocation patterns.
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  6. ▌
    Profimp · scverse
    Profile Python import time using profimp and open a waterfall HTML report. Use when investigating slow startup or wanting to identify which imports are most expensive.
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