Agent skill

Spatial Preprocess

Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData.

MITAuto-check passedResearch & Science

Install Spatial Preprocess

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a claude-code

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw spatial-preprocess --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/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/spatial/spatial-preprocess .claude/skills/spatial-preprocess && 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/

Paste into Claude Code

Install the "spatial-preprocess" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess into .claude/skills/spatial-preprocess/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-preprocess", 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

$skill-installer install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess

Type 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.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw spatial-preprocess --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/spatial/spatial-preprocess .agents/skills/spatial-preprocess && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "spatial-preprocess" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess into .agents/skills/spatial-preprocess/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-preprocess", 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.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw spatial-preprocess --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/spatial/spatial-preprocess .cursor/skills/spatial-preprocess && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "spatial-preprocess" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess into .cursor/skills/spatial-preprocess/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-preprocess", 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

$ gemini skills install https://github.com/TianGzlab/OmicsClaw.git --path skills/spatial/spatial-preprocess

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw spatial-preprocess --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/spatial/spatial-preprocess .gemini/skills/spatial-preprocess && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "spatial-preprocess" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess into .gemini/skills/spatial-preprocess/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-preprocess", 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.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw spatial-preprocess

Installs 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).

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/spatial/spatial-preprocess .github/skills/spatial-preprocess && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "spatial-preprocess" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess into .github/skills/spatial-preprocess/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-preprocess", 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.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw spatial-preprocess --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/spatial/spatial-preprocess .opencode/skills/spatial-preprocess && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "spatial-preprocess" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess into .opencode/skills/spatial-preprocess/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-preprocess", 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-preprocess
GitHub stars: 161
Token cost: ~1.6k tokens
SKILL.md length: 510 words
Files: 10 (incl. references)
Skills in repo: 95
Repo updated: Jul 28, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData.

Works in 7 steps: Load AnnData (--input) or build a… → Apply tissue preset if --tissue is given… → QC + filter spots / genes;… → …
Tasks that involve Bioinformatics
SKILL.md covers When to use, Inputs & Outputs, Flow and Gotchas, plus 2 more sections
Runs Python and R scripts from its folder; calls python

What it does

Spatial Preprocess is an agent skill from TianGzlab/OmicsClaw. Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData. Skip when raw FASTQs need converting first (use spatial-raw-processing); tissue-domain detection on already-preprocessed data (use spatial-domains).

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `r_visualization/README.md`, `references/methodology.md` and `references/output_contract.md`).

It sits in Research & Science, covering Bioinformatics. It works with AnnData and UMAP. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is MIT.

When your agent uses it

Tasks that involve Bioinformatics

Example prompts

“/spatial-preprocess”

Requirements

Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

Load AnnData (--input) or build a synthetic spatial demo.
Apply tissue preset if --tissue is given (overrides default --min-genes / --min-cells / --max-mt-pct / --max-genes).
QC + filter spots / genes; mitochondrial-percentage filter uses --species for gene prefix (MT- for human, mt- for mouse).
Normalise (CP10k log) → HVG (--n-top-hvg) → PCA (--n-pcs).
Build neighbour graph (--n-neighbors) → Leiden at --leiden-resolution.
If --resolutions a,b,c,... is set, sweep additional Leiden resolutions and write the multi-resolution table.
Save processed.h5ad, tables, figures, report.md, result.json.

What it can do on your machine

Read from SKILL.md and the folder at commit 6fbd79f. 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
Ships script files (Python and R), which the agent can run.
Shell commands in SKILL.md call:
- python
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 Preprocess loads about 1.6k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 510 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~88
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~5.2k

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 TianGzlab/OmicsClaw at commit 6fbd79f, republished under its MIT licence (© TianGzlab). 510 words, ~1,637 tokens.

Download SKILL.mdSave it as .claude/skills/spatial-preprocess/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.

name: spatial-preprocess
description: Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData. Skip when raw FASTQs need converting first (use spatial-raw-processing); tissue-domain detection on already-preprocessed data (use spatial-domains).
version: 0.6.0
author: OmicsClaw
license: MIT
emoji: 🔬
tags: spatial, visium, xenium, preprocessing, qc, normalization, hvg, pca, leiden
requires: anndata, matplotlib, numpy, pandas, scanpy, scipy, seaborn

spatial-preprocess

When to use

The user has a spatial AnnData (Visium / Xenium / SpaceRanger output / generic) — either freshly loaded or coming out of spatial-raw-processing — and wants the canonical "QC → filter → normalise → HVG → PCA → neighbours → Leiden" path producing a downstream-ready processed.h5ad. This is the foundation skill — most other spatial analyses (spatial-domains, spatial-de, spatial-genes, spatial-deconv, spatial-communication, ...) consume its output. Single backend: scanpy_standard.

For raw FASTQ → matrix conversion use spatial-raw-processing. For explicit tissue-domain detection (SpaGCN / STAGATE) on top of this output use spatial-domains.

Inputs & Outputs

<!-- AUTO-GENERATED from skill.yaml (interface) — do not edit by hand. Regenerate: python scripts/generate_skill_md.py <skill_dir> -->

Inputs

Input kinds: file, directory
Modalities: visium, xenium
File types: .h5ad, .h5, .hdf5, .zarr
Expects obsm: spatial

Outputs

tables/cluster_summary.csv
tables/multi_resolution_summary.csv
tables/pca_variance_ratio.csv
tables/preprocess_run_summary.csv
tables/preprocess_spatial_points.csv
tables/preprocess_umap_points.csv
tables/qc_metric_distributions.csv
tables/qc_summary.csv
figures/cluster_size_barplot.png
figures/leiden_resolution_sweep.png
figures/pca_variance_curve.png
figures/qc_metric_distributions.png
figures/qc_metrics_spatial.png
figures/spatial_leiden.png
figures/umap_leiden.png
processed.h5ad
report.md
result.json
Processed AnnData (saves_h5ad) — adds obs: leiden; obsm: spatial, X_pca, X_umap; var: highly_variable; layers: counts
AnnData processing state after success: preprocessed

Flow

Load AnnData (--input) or build a synthetic spatial demo.
Apply tissue preset if --tissue <preset> is given (overrides default --min-genes / --min-cells / --max-mt-pct / --max-genes).
QC + filter spots / genes; mitochondrial-percentage filter uses --species for gene prefix (MT- for human, mt- for mouse).
Normalise (CP10k log) → HVG (--n-top-hvg) → PCA (--n-pcs).
Build neighbour graph (--n-neighbors) → Leiden at --leiden-resolution.
If --resolutions a,b,c,... is set, sweep additional Leiden resolutions and write the multi-resolution table.
Save processed.h5ad, tables, figures, report.md, result.json.

Gotchas

All input + parameter validation goes through parser.error (exit code 2), not ValueError / SystemExit(1). spatial_preprocess.py:1006 for missing --input; :1008 for missing path; :1010 for unknown --tissue; :1012-1028 for negative / out-of-range numeric flags. Wrappers expecting standard ValueError need to handle exit code 2 separately.
result.json["n_pcs_used"] may be smaller than the requested --n-pcs. The dimensionality is clipped in the helper at skills/spatial/_lib/preprocessing.py:307 (cross-file anchor — lint skips). The clipped value is surfaced into the per-row metrics CSV at spatial_preprocess.py:193 (n_pcs_used) and :191 (n_pcs_requested). Pass n_pcs_used (not n_pcs_requested) to downstream skills like spatial-domains --n-pcs.
--tissue overrides numeric defaults silently. When a preset matches, TISSUE_PRESETS rewrites --min-genes / --min-cells / --max-mt-pct / --max-genes from the preset table. Pass values explicitly to override; result.json["effective_params"] records what was actually applied.
No UMAP fallback — missing umap-learn aborts the run. sc.tl.umap is called unconditionally; if umap-learn (or igraph for Leiden) isn't installed, the run raises ImportError and exits non-zero. There is no _safe_umap shim in the script — install umap-learn and igraph before running on a fresh env.
--resolutions parsing errors via parser.error. spatial_preprocess.py:1026 raises on malformed comma-separated values; :1028 raises if any value is <= 0. Format: 0.4,0.6,0.8,1.0 (no spaces inside the value).
Default --tissue is None (no preset). When unset, the skill uses the generic defaults from defaults dict at :956-958 — typically the right call. Tissue presets (brain, tumor, etc.) tighten thresholds and may filter aggressively on tissues with low UMI counts.

Show full SKILL.md (61 more words)Show less

Key CLI

bash

# Demo (synthetic Visium)
python omicsclaw.py run spatial-preprocess --demo --output /tmp/spatial_pp_demo

# Visium with default presets
python omicsclaw.py run spatial-preprocess \
  --input visium.h5ad --output results/

# Tissue preset + custom resolution
python omicsclaw.py run spatial-preprocess \
  --input visium.h5ad --output results/ \
  --data-type visium --tissue brain --leiden-resolution 1.2

# Multi-resolution sweep for picking optimal clustering
python omicsclaw.py run spatial-preprocess \
  --input visium.h5ad --output results/ \
  --resolutions 0.4,0.6,0.8,1.0,1.4

# Mouse Xenium
python omicsclaw.py run spatial-preprocess \
  --input xenium.h5ad --output results/ \
  --data-type xenium --species mouse --max-mt-pct 15

Files

SKILL.md and 9 other files (references) in skills/spatial/spatial-preprocess of TianGzlab/OmicsClaw.

SKILL.md
r_visualization/README.md
r_visualization/preprocess_publication_template.R
references/methodology.md
references/output_contract.md
references/parameters.md
skill.yaml
spatial_preprocess.py
tests/__init__.py
tests/test_spatial_preprocess.py

Open the folder on GitHubat commit 6fbd79f

Compare with similar skills

Spatial Preprocess 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 Preprocess compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Spatial Preprocess this skillTianGzlab/OmicsClaw	161	—	~1.6k	Automated safety check: Pass	MIT	2 mo ago
ScanpyK-Dense-AI/scientific-agent-skills	48k	1 repos	~5.1k	Automated safety check: Pass	BSD-3-Clause	2 days ago
Bio Tcr Bcr Analysis Scirpy AnalysisGPTomics/bioSkills	1.2k	1 repos	~4.4k	Automated safety check: Pass	MIT	1 mo ago
Scanpyaipoch/medical-research-skills	2k	—	~3.9k	Automated safety check: Pass	MIT	20 days ago
Scarf Single CellNygenAnalytics/scarf	126	—	~4.7k	Automated safety check: Pass	BSD-3-Clause	today
Scanpy Single-Cell Analysisdavila7/claude-code-templates	32k	16 repos	~2.8k	Automated safety check: Pass	MIT	today

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Questions about Spatial Preprocess

What does Spatial Preprocess do?

Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData. Spatial Preprocess is an agent skill from TianGzlab/OmicsClaw. Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData.

When should I use Spatial Preprocess?

Spatial Preprocess fits situations like: tasks that involve Bioinformatics.

How do I install Spatial Preprocess in Claude Code?

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

How do I install Spatial Preprocess in Codex?

Run `npx skills add TianGzlab/OmicsClaw --skill spatial-preprocess -a codex`. Or copy the skill folder (skills/spatial/spatial-preprocess in TianGzlab/OmicsClaw) into .agents/skills/spatial-preprocess in your project. Codex loads it when a task matches its description.

Can I use Spatial Preprocess 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 TianGzlab/OmicsClaw --skill spatial-preprocess -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-preprocess, .gemini/skills/spatial-preprocess, .github/skills/spatial-preprocess and .opencode/skills/spatial-preprocess in your project.

What does Spatial Preprocess need to run?

Going by SKILL.md and its folder, Spatial Preprocess needs Python and R for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Spatial Preprocess 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 Preprocess 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 Preprocess use?

Spatial Preprocess 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 Preprocess use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.6k tokens, read only when the agent opens those files.

What are the alternatives to Spatial Preprocess?

Skills that share tags, products or a category with Spatial Preprocess: Scanpy (K-Dense-AI/scientific-agent-skills, 48k stars), Bio Tcr Bcr Analysis Scirpy Analysis (GPTomics/bioSkills, 1.2k stars), Scanpy (aipoch/medical-research-skills, 2k stars) and Scarf Single Cell (NygenAnalytics/scarf, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Spatial Preprocess?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on July 28, 2026.

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

Install Spatial Preprocess

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

spatial-preprocess

When to use

Inputs & Outputs

Flow

Gotchas

Key CLI

See also

Files

Compare with similar skills

Similar skills

More from TianGzlab/OmicsClaw

Related

Questions about Spatial Preprocess