Scarf Single Cell
NygenAnalytics/scarf
Analyze single-cell data with core Scarf, the out-of-core Zarr DataStore library with immutable artifacts and pipeline runs.
Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots.
Install Spatial S2 Normalize Cluster
$ npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install QING1105/ezST spatial-s2-normalize-cluster --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/s2-normalize-cluster .claude/skills/spatial-s2-normalize-cluster && rm -rf skills-srcUse ~/.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/
Install the "spatial-s2-normalize-cluster" agent skill from https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster into .claude/skills/spatial-s2-normalize-cluster/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-s2-normalize-cluster", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-clusterType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install QING1105/ezST spatial-s2-normalize-cluster --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/s2-normalize-cluster .agents/skills/spatial-s2-normalize-cluster && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "spatial-s2-normalize-cluster" agent skill from https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster into .agents/skills/spatial-s2-normalize-cluster/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-s2-normalize-cluster", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install QING1105/ezST spatial-s2-normalize-cluster --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/s2-normalize-cluster .cursor/skills/spatial-s2-normalize-cluster && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "spatial-s2-normalize-cluster" agent skill from https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster into .cursor/skills/spatial-s2-normalize-cluster/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-s2-normalize-cluster", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/QING1105/ezST.git --path plugins/spatial-transcriptomics/skills/s2-normalize-cluster--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install QING1105/ezST spatial-s2-normalize-cluster --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/s2-normalize-cluster .gemini/skills/spatial-s2-normalize-cluster && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "spatial-s2-normalize-cluster" agent skill from https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster into .gemini/skills/spatial-s2-normalize-cluster/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-s2-normalize-cluster", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install QING1105/ezST spatial-s2-normalize-clusterInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/s2-normalize-cluster .github/skills/spatial-s2-normalize-cluster && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "spatial-s2-normalize-cluster" agent skill from https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster into .github/skills/spatial-s2-normalize-cluster/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-s2-normalize-cluster", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install QING1105/ezST spatial-s2-normalize-cluster --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/QING1105/ezST.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/spatial-transcriptomics/skills/s2-normalize-cluster .opencode/skills/spatial-s2-normalize-cluster && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "spatial-s2-normalize-cluster" agent skill from https://github.com/QING1105/ezST/tree/master/plugins/spatial-transcriptomics/skills/s2-normalize-cluster into .opencode/skills/spatial-s2-normalize-cluster/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-s2-normalize-cluster", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
spatial-s2-normalize-cluster- GitHub stars
- 101
- Token cost
- ~428 tokens
- SKILL.md length
- 128 words
- Files
- 1
- Skills in repo
- 11
- Repo updated
- First seen
- Licence
- MIT
At a glance
Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots.
- Works in 2 steps: Normalize:… → Cluster:…
- The user asks to normalize
- SKILL.md covers Goal, Steps, Outputs and Biological Interpretation, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Spatial S2 Normalize Cluster is an agent skill from QING1105/ezST. 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.
Its SKILL.md is about 430 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. It works with UMAP. 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 user asks to normalize
- Find highly variable genes
- Cluster Visium spots (PCA/UMAP/Leiden)
Example prompts
- “/spatial-s2-normalize-cluster”
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Normalize: normalize_visium(adata_path=..., output_path=..., plot_path=..., target_sum=..., n_top_genes=...)
- Cluster: cluster_spatial_data(adata_path=..., output_path=..., plot_path=..., n_pcs=..., resolution=...)
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 S2 Normalize Cluster loads about 428 tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 128 words of instructions outside code blocks.
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). 128 words, ~428 tokens.
Download SKILL.mdSave it as
.claude/skills/spatial-s2-normalize-cluster/SKILL.md (or your agent's skills folder).- name
- spatial-s2-normalize-cluster
- description
- 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.
- license
- MIT
S2 — Normalize + Cluster
Goal
Normalize and log-transform the filtered data, then cluster spots with the standard PCA + UMAP + Leiden pipeline.
Steps
- 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.
- Defaults:
- 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.
- Defaults:
Outputs
results/03_normalization/<sample>_normalized.h5adresults/04_clustering/<sample>_clustered.h5adresults/04_clustering/<sample>_umap.pngresults/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.
© 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/s2-normalize-cluster of QING1105/ezST.
Open the folder on GitHubat commit 429f9fc
Compare with similar skills
Spatial S2 Normalize Cluster 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Spatial S2 Normalize Cluster this skillQING1105/ezST | 101 | — | ~428 | Automated safety check: Pass | MIT | |
| Scarf Single CellNygenAnalytics/scarf | 126 | — | ~5.6k | Automated safety check: Pass | BSD-3-Clause | |
| ScanpyK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~5.1k | Automated safety check: Pass | BSD-3-Clause | |
| Umap Tsne Analysisaipoch/medical-research-skills | 2k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Bio Single Cell ClusteringGPTomics/bioSkills | 1.2k | 1 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Bio Spatial Transcriptomics Spatial VisualizationGPTomics/bioSkills | 1.2k | 1 repos | ~3.6k | Automated safety check: Pass | MIT |
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More from QING1105/ezST
All 11 skills in this repoSpatial Transcriptomics
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End-to-end 10x Visium spatial transcriptomics analysis workflow with staged execution and human review gates.
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.
Spatial Atera
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Atera platform branch of the spatial transcriptomics workflow — load and validate Atera cell-level output (AnnData + Zarr segmentation) for downstream analysis.
Spatial S3 Domains SVG
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Stage 3 of the spatial transcriptomics workflow — identify spatial domains and detect spatially variable genes.
Spatial S4 Deconvolve
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Stage 4 of the spatial transcriptomics workflow — deconvolve Visium spots into cell-type proportions.
Spatial S5 Downstream
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Stage 5 of the spatial transcriptomics workflow — neighborhood enrichment and cell-cell communication analysis.
Related
Works with
Categories
Questions about Spatial S2 Normalize Cluster
What does Spatial S2 Normalize Cluster do?
Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots. Spatial S2 Normalize Cluster is an agent skill from QING1105/ezST. Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots.
When should I use Spatial S2 Normalize Cluster?
Spatial S2 Normalize Cluster fits situations like: the user asks to normalize; find highly variable genes; cluster Visium spots (PCA/UMAP/Leiden).
How do I install Spatial S2 Normalize Cluster in Claude Code?
Run `npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a claude-code`. Or copy the skill folder (plugins/spatial-transcriptomics/skills/s2-normalize-cluster in QING1105/ezST) into .claude/skills/spatial-s2-normalize-cluster in your project. Claude Code loads it when a task matches its description.
How do I install Spatial S2 Normalize Cluster in Codex?
Run `npx skills add QING1105/ezST --skill spatial-s2-normalize-cluster -a codex`. Or copy the skill folder (plugins/spatial-transcriptomics/skills/s2-normalize-cluster in QING1105/ezST) into .agents/skills/spatial-s2-normalize-cluster in your project. Codex loads it when a task matches its description.
Can I use Spatial S2 Normalize Cluster 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-s2-normalize-cluster -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-s2-normalize-cluster, .gemini/skills/spatial-s2-normalize-cluster, .github/skills/spatial-s2-normalize-cluster and .opencode/skills/spatial-s2-normalize-cluster in your project.
What does Spatial S2 Normalize Cluster need to run?
SKILL.md names no scripts, command-line tools or credentials: Spatial S2 Normalize Cluster is instructions for the agent only.
Does Spatial S2 Normalize Cluster 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 S2 Normalize Cluster 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 S2 Normalize Cluster use?
Spatial S2 Normalize Cluster 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 S2 Normalize Cluster use?
About 428 tokens (SKILL.md is roughly 1.7k 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 S2 Normalize Cluster?
Skills that share tags, products or a category with Spatial S2 Normalize Cluster: Scarf Single Cell (NygenAnalytics/scarf, 126 stars), Scanpy (K-Dense-AI/scientific-agent-skills, 48k stars), Umap Tsne Analysis (aipoch/medical-research-skills, 2k stars) and Bio Single Cell Clustering (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Spatial S2 Normalize Cluster?
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.