Spatial Visium

Community

QING1105

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.

Overview

Publisher	QING1105
Repository	`ezST`
Skill name	`spatial-visium`
Stars	101
Forks	1
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by QING1105 on GitHub. Read the source before you install it.

Installation

Install the Spatial Visium AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/spatial-visium
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/spatial-visium

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/QING1105/ezST.git /tmp/ezST
mkdir -p .claude/skills
cp -r /tmp/ezST/plugins/spatial-transcriptomics/skills/spatial-visium .claude/skills/spatial-visium

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spatial Visium in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Spatial Visium on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Spatial Visium is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Frequently asked questions

What does the Spatial Visium AI skill do?

Why use Spatial Visium on TypingMind?

Because you install it once and use it with any model. Spatial Visium is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Spatial Visium in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/spatial-visium. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Spatial Visium?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Spatial Visium?

As many as you like. As long as a model supports skills, you can use Spatial Visium with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Spatial Visium AI skill free?

Yes. It is published on GitHub by QING1105 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Classic Visium Branch — Spot-Level 5-Stage Pipeline

Goal

Stages

S1 — Load + QC

S2 — Normalize + Cluster

S3 — Spatial Domains + SVG

S4 — Deconvolution (required for classic Visium)

S5 — Prepare for Shared Downstream

Outputs

Biological Interpretation

Stop for Review

Frequently asked questions

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