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Agent skill

Spatial Visium

by QING1105 in QING1105/ezST

Classic Visium platform branch of the spatial transcriptomics workflow — spot-level 5-stage pipeline (load+QC, normalize+cluster, domains+SVG, deconvolution, downstream prep).

MITAuto-check passedResearch & Science

Install Spatial Visium

skills CLI
$ npx skills add QING1105/ezST --skill spatial-visium -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install QING1105/ezST spatial-visium --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/spatial-visium .claude/skills/spatial-visium && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
spatial-visium
GitHub stars
101
Token cost
~750 tokens
SKILL.md length
312 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Classic Visium platform branch of the spatial transcriptomics workflow — spot-level 5-stage pipeline (load+QC, normalize+cluster, domains+SVG, deconvolution, downstream prep).

  • The users data is classic 10x Visium spot-level output (55 μm spots)
  • SKILL.md covers Goal, Stages, Outputs and Biological Interpretation, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Bioinformatics

What it does

Spatial Visium is an agent skill from QING1105/ezST. 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.

Its SKILL.md is about 750 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: 10x Visium spatial transcriptomics analysis skills for Codex — staged workflow with human review gates and LLM biological interpretation. The licence is MIT.

When your agent uses it

  • The users data is classic 10x Visium spot-level output (55 μm spots)
  • Tasks that involve Bioinformatics

Example prompts

  • “/spatial-visium”

What it can do on your machine

Read from SKILL.md and the folder at commit 429f9fc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Spatial Visium loads about 750 tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 312 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~750

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from QING1105/ezST at commit 429f9fc, republished under its MIT licence (© QING1105). 312 words, ~750 tokens.

Download SKILL.mdSave it as .claude/skills/spatial-visium/SKILL.md (or your agent's skills folder).
name
spatial-visium
description
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.
license
MIT

Classic Visium Branch — Spot-Level 5-Stage Pipeline

Goal

Run the classic Visium spot-level analysis. Spots are 55 μm and cover multiple cells — deconvolution IS required to estimate cell-type composition (unlike Visium HD / Xenium / Atera which are cell-resolved).

Stages

S1 — Load + QC
  • Tool chain: init_spatial_project → load_visium_data → filter_visium_spots
  • Plots: QC violin plots / spot count distributions before and after filtering
  • Results: filtered h5ad, QC summary
  • Review focus: are the filtering thresholds (min_counts, min_genes, pct_mt) reasonable? Is the retained spot count consistent with the expected tissue size?
S2 — Normalize + Cluster
  • Tool chain: normalize_visium → cluster_spatial_data
  • Plots: UMAP, spatial scatter colored by cluster
  • Results: normalized/clustered h5ad
  • Review focus: is the number of clusters biologically plausible? Do clusters segregate spatially?
S3 — Spatial Domains + SVG
  • Tool chain: identify_spatial_domains → find_spatially_variable_genes
  • Plots: spatial domain map, top SVG spatial plots
  • Results: domain assignments, SVG ranked CSV
  • Review focus: do spatial domains match known tissue architecture? Do top SVGs make biological sense?
S4 — Deconvolution (required for classic Visium)
  • Tool chain: deconvolve_spatial_destvi (preferred) or deconvolve_spatial_spotlight
  • Requires reference scRNA-seq h5ad with cell-type labels in obs
  • Plots: cell-type proportion spatial maps, proportion stacked bar
  • Results: proportions CSV per spot, deconvolved h5ad
  • Review focus: are the dominant cell types consistent with the tissue type? Any unexpected cell type dominating?
S5 — Prepare for Shared Downstream
  • Ensure spot-level h5ad has cell-type labels (from deconvolution) attached for neighborhood enrichment / CellChat.
  • If the user also wants single-cell reconstruction from classic Visium (optional, unusual), mention STIE as the morphological-deconvolution route — but this is NOT the default.

Outputs

  • results/01_loading/ ... results/07_deconvolution/ (see parent skill conventions)
  • Final: spot-level h5ad with deconvolution proportions attached

Biological Interpretation

Use the parent skill's interpretation template for each stage. For S4 specifically, cross-check dominant cell types against tissue type (e.g., gastric cancer should show epithelial/tumor cells in tumor regions).

Stop for Review

Present interpretation at every stage using the template from the parent spatial-transcriptomics skill. Wait for 通过 / 调整 / 跳过 before proceeding.

© QING1105, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/spatial-transcriptomics/skills/spatial-visium of QING1105/ezST.

Open the folder on GitHubat commit 429f9fc

Compare with similar skills

Spatial Visium next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Spatial Visium compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Spatial Visium this skillQING1105/ezST101—~750Automated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k3 repos~3.4kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
MFA Pipeline Orchestratoraiming-lab/AutoResearchClaw15k—~923Automated safety check: PassMIT

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  • Alphagenome Single Variant Analysis

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More from QING1105/ezST

All 11 skills in this repo

  • Spatial Transcriptomics

    QING1105/ezST

    End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates.

    101 GitHub stars~1.4k tokensUpdated 1 mo ago
    Auto-check passed

  • Spatial Visium Hd

    QING1105/ezST

    Visium HD platform branch of the spatial transcriptomics workflow — reconstruct single cells from 2 μm bins via morphological segmentation and bin-to-cell aggregation.

    101 GitHub stars~2.6k tokensUpdated 1 mo ago
    Auto-check passed

  • Spatial Atera

    QING1105/ezST

    Atera platform branch of the spatial transcriptomics workflow — load and validate Atera cell-level output (AnnData + Zarr segmentation) for downstream analysis.

    101 GitHub stars~576 tokensUpdated 1 mo ago
    Auto-check passed

  • Spatial S3 Domains SVG

    QING1105/ezST

    Stage 3 of the spatial transcriptomics workflow — identify spatial domains and detect spatially variable genes.

    101 GitHub stars~476 tokensUpdated 1 mo ago
    Auto-check passed

  • Spatial S4 Deconvolve

    QING1105/ezST

    Stage 4 of the spatial transcriptomics workflow — deconvolve Visium spots into cell-type proportions.

    101 GitHub stars~480 tokensUpdated 1 mo ago
    Auto-check passed

  • Spatial S5 Downstream

    QING1105/ezST

    Stage 5 of the spatial transcriptomics workflow — neighborhood enrichment and cell-cell communication analysis.

    101 GitHub stars~513 tokensUpdated 1 mo ago
    Auto-check passed

Categories

Questions about Spatial Visium

What does Spatial Visium do?

Classic Visium platform branch of the spatial transcriptomics workflow — spot-level 5-stage pipeline (load+QC, normalize+cluster, domains+SVG, deconvolution, downstream prep). Spatial Visium is an agent skill from QING1105/ezST. Classic Visium platform branch of the spatial transcriptomics workflow — spot-level 5-stage pipeline (load+QC, normalize+cluster, domains+SVG, deconvolution, downstream prep).

When should I use Spatial Visium?

Spatial Visium fits situations like: the users data is classic 10x Visium spot-level output (55 μm spots); tasks that involve Bioinformatics.

How do I install Spatial Visium in Claude Code?

Run `npx skills add QING1105/ezST --skill spatial-visium -a claude-code`. Or copy the skill folder (plugins/spatial-transcriptomics/skills/spatial-visium in QING1105/ezST) into .claude/skills/spatial-visium in your project. Claude Code loads it when a task matches its description.

How do I install Spatial Visium in Codex?

Run `npx skills add QING1105/ezST --skill spatial-visium -a codex`. Or copy the skill folder (plugins/spatial-transcriptomics/skills/spatial-visium in QING1105/ezST) into .agents/skills/spatial-visium in your project. Codex loads it when a task matches its description.

Can I use Spatial Visium in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add QING1105/ezST --skill spatial-visium -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/spatial-visium, .gemini/skills/spatial-visium, .github/skills/spatial-visium and .opencode/skills/spatial-visium in your project.

What does Spatial Visium need to run?

SKILL.md names no scripts, command-line tools or credentials: Spatial Visium is instructions for the agent only.

Does Spatial Visium access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Spatial Visium safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Spatial Visium use?

Spatial Visium is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Spatial Visium use?

About 750 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Spatial Visium?

Skills that share tags, products or a category with Spatial Visium: Dbsnp Database (google-deepmind/science-skills, 3.2k stars), Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Spatial Visium?

QING1105 (a GitHub user) maintains it in QING1105/ezST, which has 101 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on August 26, 2026.

Source: QING1105/ezST on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.