Agent skill

Sc Preprocessing

Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals.

Install Sc Preprocessing

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill sc-preprocessing -a claude-code

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-preprocessing --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/singlecell/scrna/sc-preprocessing .claude/skills/sc-preprocessing && 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 "sc-preprocessing" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-preprocessing into .claude/skills/sc-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-preprocessing", 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/singlecell/scrna/sc-preprocessing

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 sc-preprocessing -a codex

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-preprocessing --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/singlecell/scrna/sc-preprocessing .agents/skills/sc-preprocessing && 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 "sc-preprocessing" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-preprocessing into .agents/skills/sc-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-preprocessing", 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 sc-preprocessing -a cursor

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-preprocessing --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/singlecell/scrna/sc-preprocessing .cursor/skills/sc-preprocessing && 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 "sc-preprocessing" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-preprocessing into .cursor/skills/sc-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-preprocessing", 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/singlecell/scrna/sc-preprocessing

--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 sc-preprocessing -a gemini-cli

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw sc-preprocessing --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/singlecell/scrna/sc-preprocessing .gemini/skills/sc-preprocessing && 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 "sc-preprocessing" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-preprocessing into .gemini/skills/sc-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-preprocessing", 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 sc-preprocessing

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 sc-preprocessing -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/singlecell/scrna/sc-preprocessing .github/skills/sc-preprocessing && 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 "sc-preprocessing" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-preprocessing into .github/skills/sc-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-preprocessing", 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 sc-preprocessing -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 sc-preprocessing --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/singlecell/scrna/sc-preprocessing .opencode/skills/sc-preprocessing && 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 "sc-preprocessing" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-preprocessing into .opencode/skills/sc-preprocessing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-preprocessing", 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: sc-preprocessing
GitHub stars: 161
Token cost: ~1.4k tokens
SKILL.md length: 401 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 normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals.

Works in 6 steps: Load AnnData; infer species;… → Reuse existing QC if n_genes_by_counts /… → Apply shared filtering (--min-genes,… → …
Tasks that involve Bioinformatics
SKILL.md covers When to use, Inputs & Outputs, Flow and Gotchas, plus 2 more sections
Runs Python scripts from its folder; calls python

What it does

Sc Preprocessing is an agent skill from TianGzlab/OmicsClaw. Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals. Skip when QC thresholds are still undecided (use sc-qc); batch correction across samples (use sc-batch-integration).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `references/methodology.md`, `references/output_contract.md` and `references/parameters.md`).

It sits in Research & Science, covering Bioinformatics. It works with AnnData and Scanpy. 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

“/sc-preprocessing”

Requirements

Python 3

Workflow steps

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

Load AnnData; infer species; canonicalise gene-name / expression layout via the shared single-cell standardiser.
Reuse existing QC if n_genes_by_counts / total_counts / pct_counts_mt are present in obs; otherwise compute them.
Apply shared filtering (--min-genes, --min-cells, --max-mt-pct); drop doublets when predicted_doublet / doublet_score columns are present…
Run the chosen normalisation backend (scanpy / seurat / sctransform / pearson_residuals).
Select HVGs (--n-top-hvg) and compute PCA (--n-pcs).
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), 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

Sc Preprocessing loads about 1.4k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 401 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~62
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3.7k

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). 401 words, ~1,375 tokens.

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

name: sc-preprocessing
description: Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals. Skip when QC thresholds are still undecided (use sc-qc); batch correction across samples (use sc-batch-integration).
version: 0.6.0
author: OmicsClaw
license: MIT
emoji: 🧫
tags: singlecell, scrna, preprocessing, normalization, hvg, pca, scanpy, seurat, sctransform, pearson_residuals
requires: anndata, matplotlib, numpy, pandas, phate, scanpy, scipy, seaborn

sc-preprocessing

When to use

The user has a filtered, QC-annotated AnnData and wants the standard "normalise → HVG → PCA" pipeline before clustering or batch integration. Four interchangeable backends are available: scanpy (default; CP10k log + HVG seurat flavour), seurat (R-backed LogNormalize / CLR / RC), sctransform (R-backed regularised NB), and pearson_residuals (raw-count HVG selection plus Pearson residual transformation). The skill stops at PCA — UMAP / clustering live in sc-clustering, multi-sample correction in sc-batch-integration.

Inputs & Outputs

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

Inputs

Modalities: scrna
File types: .h5ad

Outputs

tables/X_norm.csv
tables/cell_metadata.csv
tables/cluster_summary.csv
tables/embedding_points.csv
tables/gene_expression.csv
tables/hvg.csv
tables/hvg_summary.csv
tables/obs.csv
tables/pca.csv
tables/pca_embedding.csv
tables/pca_variance_ratio.csv
tables/preprocess_summary.csv
tables/qc_metrics_per_cell.csv
figures/highly_variable_genes.png
figures/pca_variance.png
figures/qc_violin.png
figures/r_hvg_violin.png
analysis_summary.txt
info.json
processed.h5ad
report.md
result.json
Processed AnnData (saves_h5ad) — adds obsm: X_pca; var: highly_variable; layers: counts
AnnData processing state after success: preprocessed

Flow

Load AnnData; infer species; canonicalise gene-name / expression layout via the shared single-cell standardiser.
Reuse existing QC if n_genes_by_counts / total_counts / pct_counts_mt are present in obs; otherwise compute them.
Apply shared filtering (--min-genes, --min-cells, --max-mt-pct); drop doublets when predicted_doublet / doublet_score columns are present (opt out via --no-remove-doublets).
Run the chosen normalisation backend (scanpy / seurat / sctransform / pearson_residuals).
Select HVGs (--n-top-hvg) and compute PCA (--n-pcs).
Save processed.h5ad, tables, figures, report.md, result.json.

Show full SKILL.md (212 more words)Show less

Gotchas

result.json["n_pcs_used"] may be smaller than the requested --n-pcs. sc_preprocess.py:876 reads obsm["X_pca"].shape[1] after PCA — small matrices cap the count below the request. Trust n_pcs_used, not the input flag, when handing off to sc-clustering --n-pcs.
R-backed seurat / sctransform need a working Rscript env. sc_preprocess.py:271 raises RuntimeError("Seurat preprocessing returned no overlapping cells or genes") when the R round-trip empties the matrix; sc_preprocess.py:296 raises RuntimeError("Seurat preprocessing returned PCA rows that do not align with exported cells") when the R-side PCA shape disagrees with the cell list. Confirm Seurat, SingleCellExperiment, zellkonverter (and sctransform for that method) are installed before picking these methods.
Doublet filter is on-by-default whenever sc-doublet-detection ran. sc_preprocess.py:510-511 passes filter_doublets=True and doublet_score_threshold=0.25 when those columns exist in obs. To keep the called-doublet rows, pass --no-remove-doublets.
figure_data/gene_expression.csv write failures are silent. sc_preprocess.py:670-672 catches the exception and only logs a warning — figure_data/manifest.json is the source of truth for which figure-data files actually landed.
--input is mandatory unless --demo. sc_preprocess.py:1013 raises ValueError("--input required when not using --demo").

Key CLI

bash

# Demo (built-in synthetic data)
python omicsclaw.py run sc-preprocessing --demo --output /tmp/sc_preprocess_demo

# Default scanpy backend
python omicsclaw.py run sc-preprocessing \
  --input filtered.h5ad --output results/

# R-backed Seurat LogNormalize
python omicsclaw.py run sc-preprocessing \
  --input filtered.h5ad --output results/ \
  --method seurat --seurat-normalize-method LogNormalize

# Pearson residuals (recommended for very sparse / heterogeneous data)
python omicsclaw.py run sc-preprocessing \
  --input filtered.h5ad --output results/ \
  --method pearson_residuals --n-top-hvg 3000

Files

SKILL.md and 9 other files (references) in skills/singlecell/scrna/sc-preprocessing of TianGzlab/OmicsClaw.

SKILL.md
references/methodology.md
references/output_contract.md
references/parameters.md
references/r_visualization.md
sc_preprocess.py
skill.yaml
tests/__init__.py
tests/test_sc_preprocess.py
tests/test_sc_preprocess_methods.py

Open the folder on GitHubat commit 6fbd79f

Compare with similar skills

Sc Preprocessing 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.

Sc Preprocessing compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Sc Preprocessing this skillTianGzlab/OmicsClaw	161	—	~1.4k	Automated safety check: Pass	MIT	2 mo ago
Scanpy Single-Cell Analysisdavila7/claude-code-templates	32k	16 repos	~2.8k	Automated safety check: Pass	MIT	today
Anndatadavila7/claude-code-templates	32k	12 repos	~2.5k	Automated safety check: Pass	MIT	today
ScanpyK-Dense-AI/scientific-agent-skills	48k	1 repos	~5.1k	Automated safety check: Pass	BSD-3-Clause	2 days ago
Cellxgene CensusK-Dense-AI/scientific-agent-skills	48k	1 repos	~3.4k	Automated safety check: Notes	MIT	2 days ago
Omics ToolsDrugClaw/DrugClaw	125	—	~1.1k	Automated safety check: Pass	Apache-2.0	6 mo ago

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Questions about Sc Preprocessing

What does Sc Preprocessing do?

Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals. Sc Preprocessing is an agent skill from TianGzlab/OmicsClaw. Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals.

When should I use Sc Preprocessing?

Sc Preprocessing fits situations like: tasks that involve Bioinformatics.

How do I install Sc Preprocessing in Claude Code?

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

How do I install Sc Preprocessing in Codex?

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

Can I use Sc Preprocessing 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 sc-preprocessing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sc-preprocessing, .gemini/skills/sc-preprocessing, .github/skills/sc-preprocessing and .opencode/skills/sc-preprocessing in your project.

What does Sc Preprocessing need to run?

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

Does Sc Preprocessing 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 Sc Preprocessing 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 Sc Preprocessing use?

Sc Preprocessing 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 Sc Preprocessing use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 2.3k tokens, read only when the agent opens those files.

What are the alternatives to Sc Preprocessing?

Skills that share tags, products or a category with Sc Preprocessing: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), Anndata (davila7/claude-code-templates, 32k stars), Scanpy (K-Dense-AI/scientific-agent-skills, 48k stars) and Cellxgene Census (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sc Preprocessing?

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 Sc Preprocessing

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

sc-preprocessing

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 Sc Preprocessing