Data & Analytics

--> --- name: bio-single-cell-metabolite-communication description: Analyze 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. tool_type: python primary_tool: MeboCost measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. all...

--> --- name: bio-single-cell-lineage-tracing description: Reconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories. tool_type: python primary_tool: Cassiopeia measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: biostudio-alpha-sc description: Run BioTuring's BioStudio Alpha SC GPU stack to accelerate single-cell and spatial multi-omics analysis on NVIDIA Blackwell-class hardware. keywords: - single-cell - gpu-acceleration - biostudio - spatial-transcriptomics - multi-omics measurable_outcome: Process a 1+ million cell atlas (10x HDF5 or FASTQ) end-to-end in BioStudio Alpha SC with QC, clustering, and annotation layers inside one work session. license: Proprietary (BioTuring EULA) metad...

--> <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE --> --- name: 'scprint2-foundation-model-agent' description: 'Agentic skill for using scPRINT-2, the next-generation single-cell foundation model from the Cantini Lab, for cell-type annotation, embedding, and downstream single-cell analysis.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools: - read_file - run_shell_command - web_fetch ---

--> --- name: 'xlsx' description: '"Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas"' measurable_ou...

--> --- name: "spreadsheet" description: "Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified." measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

Load when running spatial autocorrelation / hotspot / co-occurrence / neighbourhood-enrichment / Ripley K stats on a clustered spatial AnnData via squidpy. Skip when ranking spatially variable genes (use spatial-genes) or for tissue domain detection (use spatial-domains).

Load when aligning multiple spatial slices into a common coordinate frame on a multi-slice spatial AnnData via PASTE optimal transport or STalign image-aware registration. Skip when data is single-slice (no registration needed) or for cross-sample integration in the gene-expression space (use spatial-integrate).

Load when converting spatial transcriptomics raw FASTQ pairs through ST-Pipeline into a `raw_counts.h5ad` ready for spatial-preprocess. Skip when input is already a count-matrix AnnData (go straight to spatial-preprocess) or for non-spatial bulk / scRNA FASTQ (use bulkrna-read-qc / sc-fastq-qc).

Load when extracting a niche / microenvironment subset around a center cell-type by spatial radius from a labelled spatial AnnData, producing a smaller AnnData of centers + their within-radius neighbours. Skip when running global tissue-domain detection (use spatial-domains) or for cross-condition comparison (use spatial-condition).

Load when ranking spatially variable genes (SVGs) on a preprocessed spatial AnnData via Moran's I, SpatialDE, SPARK-X, or FlashS. Skip when detecting tissue domains (use spatial-domains) or for differential expression between groups (use spatial-de).

Load when detecting tissue domains / niches on a preprocessed spatial AnnData via Leiden / Louvain (spatial-weighted) or graph-neural backends (SpaGCN / STAGATE / GraphST / BANKSY / CellCharter). Skip when ranking spatially variable genes (use spatial-genes) or for spot-level cell-type annotation (use spatial-annotate).

Load when comparing two or more experimental conditions (treatment vs control) on a multi-sample preprocessed spatial AnnData via PyDESeq2 pseudobulk or Wilcoxon DE — needs `obs[condition_key]`, `obs[sample_key]`, and cluster labels. Skip when running per-cluster DE on one condition (use `spatial-de`) or comparing two slices without replicates.

Load when assigning per-spot cell-type labels on a spatial AnnData via marker-gene scoring or scRNA-reference mapping (Tangram / scANVI / CellAssign). Skip when computing spot-level cell-type proportions for multi-cell-per-spot platforms (use spatial-deconv) or for tissue-domain detection (use spatial-domains).

Multi-method consensus over spatial-domains. Fans out 5 methods in parallel, computes a SACCELERATOR-style base-clustering ranking, runs typed consensus (kmode / weighted / LCA), and emits a verified consensus report with the mandatory A-path banner per ADR 0010.

Load when merging multiple single-sample scRNA-seq count matrices (one per sample-from-sc-count) into a single downstream-ready AnnData with sample labels. Skip when input is one already-merged AnnData (use sc-standardize-input) or for FASTQ→counts on each sample (use sc-count first).

Load when aggregating single cells into metacells (sample-aware coarse-grained pseudo-cells) on a normalised scRNA AnnData via SEACells or KMeans on a low-D embedding. Skip when ranking marker genes per cluster (use sc-markers) or for trajectory pseudotime ordering (use sc-pseudotime).

Load 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).

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) or for marker-based cell-type labelling (use sc-cell-annotation).

Multi-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.

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) or no XL-MS experiment was performed.

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 `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`).

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) or when peptide-level QC is needed (use `proteomics-identification`).

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) or when working with protein-quantification tables (use `proteomics-ms-qc`).