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Spatial S2 Normalize Cluster

Community
QING1105
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.

Overview

PublisherQING1105
RepositoryezST
Skill namespatial-s2-normalize-cluster
Stars
101
Forks
1
Bundled files
Instructions only
LicenseMIT
Links
  • 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 S2 Normalize Cluster AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/s2-normalize-cluster .claude/skills/spatial-s2-normalize-cluster
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spatial S2 Normalize Cluster 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 S2 Normalize Cluster 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 S2 Normalize Cluster 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.

S2 — Normalize + Cluster

Goal

Normalize and log-transform the filtered data, then cluster spots with the standard PCA + UMAP + Leiden pipeline.

Steps

  1. Normalize: normalize_visium(adata_path=..., output_path=..., plot_path=..., target_sum=..., n_top_genes=...)
    • Defaults: target_sum=None (scanpy default), n_top_genes=2000.
    • Output: normalized h5ad + HVG plot.
  2. Cluster: cluster_spatial_data(adata_path=..., output_path=..., plot_path=..., n_pcs=..., resolution=...)
    • Defaults: n_pcs=30, resolution=1.0 (adjust if clusters look over/under-split).
    • Output: clustered h5ad + UMAP plot + spatial scatter colored by cluster.

Outputs

  • results/03_normalization/<sample>_normalized.h5ad
  • results/04_clustering/<sample>_clustered.h5ad
  • results/04_clustering/<sample>_umap.png
  • results/04_clustering/<sample>_spatial_clusters.png

Biological Interpretation

  • Report the number of clusters found and their spatial distribution.
  • Look at the spatial scatter: do clusters form contiguous spatial regions (tissue architecture) or scattered noise?
  • If clusters are too fragmented / too coarse, suggest resolution adjustment.

Stop for Review

Present interpretation using the template from the parent spatial-transcriptomics skill. Wait for 通过 / 调整 / 跳过 before proceeding to S3.

Frequently asked questions

What does the Spatial S2 Normalize Cluster AI skill do?

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.

Why use Spatial S2 Normalize Cluster on TypingMind?

Because you install it once and use it with any model. Spatial S2 Normalize Cluster 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 S2 Normalize Cluster in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Spatial S2 Normalize Cluster?

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 S2 Normalize Cluster?

As many as you like. As long as a model supports skills, you can use Spatial S2 Normalize Cluster with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Spatial S2 Normalize Cluster 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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