ezST

In the author's own words, from each skill's SKILL.md.

• spatial-s1-load-qc

  Stage 1 of the spatial transcriptomics workflow — load 10x Visium data and QC-filter low-quality spots. Use when the user asks to load Visium data, initialize a spatial project, or filter spots by quality (counts, genes, mitochondrial fraction). Produces QC plots and a filtered AnnData, then stops for review.

  plugins/spatial-transcriptomics/skills/s1-load-qc/SKILL.md on GitHub (opens in a new tab)

• spatial-s2-normalize-cluster

  Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots. Use when the user asks to normalize, log-transform, find highly variable genes, or cluster Visium spots (PCA/UMAP/Leiden). Produces UMAP and spatial cluster plots, then stops for review.

  plugins/spatial-transcriptomics/skills/s2-normalize-cluster/SKILL.md on GitHub (opens in a new tab)

• spatial-s3-domains-svg

  Stage 3 of the spatial transcriptomics workflow — identify spatial domains and detect spatially variable genes. Use when the user asks to find spatial domains/niches, run Moran's I, or detect spatially variable genes (SVGs) in Visium data. Produces spatial domain maps and SVG rankings, then stops for review.

  plugins/spatial-transcriptomics/skills/s3-domains-svg/SKILL.md on GitHub (opens in a new tab)

• spatial-s4-deconvolve

  Stage 4 of the spatial transcriptomics workflow — deconvolve Visium spots into cell-type proportions. Use when the user asks to deconvolve spatial spots, estimate cell-type composition, or run DestVI / SPOTlight with a scRNA-seq reference. Produces cell-type proportion maps, then stops for review.

  plugins/spatial-transcriptomics/skills/s4-deconvolve/SKILL.md on GitHub (opens in a new tab)

• spatial-s5-downstream

  Stage 5 of the spatial transcriptomics workflow — neighborhood enrichment and cell-cell communication analysis. Use when the user asks to compute cell-type neighborhood enrichment (co-localization) or infer cell-cell communication with CellChat on Visium data. Produces enrichment heatmaps and CellChat network plots, then stops for review.

  plugins/spatial-transcriptomics/skills/s5-downstream/SKILL.md on GitHub (opens in a new tab)

• spatial-atera

  Atera platform branch of the spatial transcriptomics workflow — load and validate Atera cell-level output (AnnData + Zarr segmentation) for downstream analysis. Use when the user's data is from the 10x Atera In Situ instrument (whole-transcriptome in situ, 2026). Produces a cell-level h5ad, then stops for review.

  plugins/spatial-transcriptomics/skills/spatial-atera/SKILL.md on GitHub (opens in a new tab)

• spatial-downstream

  Shared downstream analysis for all spatial platforms (Visium, Visium HD, Xenium, Atera) — neighborhood enrichment, CellChat cell-cell communication, and RNA velocity. Use after any platform branch has produced an h5ad with cell-type labels (classic Visium: deconvolution proportions; Visium HD / Xenium / Atera: cell-type annotations). Produces communication and trajectory results, then stops for r…

  plugins/spatial-transcriptomics/skills/spatial-downstream/SKILL.md on GitHub (opens in a new tab)

• spatial-transcriptomics

  End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates. Use when the user asks to analyze Visium / spatial transcriptomics data, run spatial analysis pipelines (load, QC, normalize, cluster, spatial domains, SVG, deconvolution, neighborhood enrichment, cell-cell communication), or interpret spatial plots biologically. Each stage produces plots…

  plugins/spatial-transcriptomics/skills/spatial-transcriptomics/SKILL.md on GitHub (opens in a new tab)

• spatial-visium-hd

  Visium HD platform branch of the spatial transcriptomics workflow — reconstruct single cells from 2 μm bins via morphological segmentation and bin-to-cell aggregation. Use when the user's data has binned_outputs/square_002um (Visium HD Space Ranger output). Produces a cell-level h5ad, then stops for review.

  plugins/spatial-transcriptomics/skills/spatial-visium-hd/SKILL.md on GitHub (opens in a new tab)

• spatial-visium

  Classic Visium platform branch of the spatial transcriptomics workflow — spot-level 5-stage pipeline (load+QC, normalize+cluster, domains+SVG, deconvolution, downstream prep). Use when the user's data is classic 10x Visium spot-level output (55 μm spots). Produces a spot-level h5ad + deconvolution results, then stops for review.

  plugins/spatial-transcriptomics/skills/spatial-visium/SKILL.md on GitHub (opens in a new tab)

• spatial-xenium

  Xenium platform branch of the spatial transcriptomics workflow — load and validate the platform's cell-level matrix for downstream analysis. Use when the user's data is 10x Xenium output (cell_feature_matrix.h5, cell-level h5ad, or transcripts.csv + segmentation). Produces a cell-level h5ad, then stops for review.

  plugins/spatial-transcriptomics/skills/spatial-xenium/SKILL.md on GitHub (opens in a new tab)

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