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Agent skill

Single Cell Rna Analysis

by PKU-YuanGroup in PKU-YuanGroup/OpenAI4S

Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo…

MITAuto-check passedResearch & Science

Install Single Cell Rna Analysis

skills CLI
$ npx skills add PKU-YuanGroup/OpenAI4S --skill single-cell-rna-analysis -a claude-code

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

GitHub CLI
$ gh skill install PKU-YuanGroup/OpenAI4S single-cell-rna-analysis --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/PKU-YuanGroup/OpenAI4S.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/single-cell-rna-analysis .claude/skills/single-cell-rna-analysis && 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/

Facts

Skill name
single-cell-rna-analysis
GitHub stars
622
Token cost
~1.3k tokens
SKILL.md length
364 words
Files
11 (incl. references)
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo…

  • Works in 4 steps: Read the input contract, select exactly… → Run preflight(config). Do not proceed… → Show the user warnings about ambient… → …
  • Cell-called GEX matrices
  • SKILL.md covers Before running, Call the workflow, Stage routing and Non-negotiable interpretation…
  • Runs Python scripts from its folder

What it does

Single Cell Rna Analysis is an agent skill from PKU-YuanGroup/OpenAI4S. Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo DA; plus preflight validation, optional explicitly requested Harmony, checkpoints, resume, and a checksummed analysis bundle. Use for cell-called GEX matrices, not FASTQ, CITE-seq, ATAC, Multiome, spatial, trajectory, communication, or CNV analysis.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `README.md`, `README_zh.md` and `kernel.py`).

It sits in Research & Science, covering Bioinformatics. It works with Scanpy. The repository describes itself as: Open-source AI agent for scientific research. Analyze data in Python/R with Claude, GPT, Gemini, and more. The licence is MIT.

When your agent uses it

  • Cell-called GEX matrices
  • Tasks that involve Bioinformatics

Example prompts

  • “/single-cell-rna-analysis”

Requirements

  • Python 3

Workflow steps

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

  1. Read the input contract, select exactly one
  2. Run preflight(config). Do not proceed when status is invalid.
  3. Show the user warnings about ambient RNA, confounding, annotation evidence,
  4. Harmony is opt-in only. Never infer a batch key or silently replace a

What it can do on your machine

Read from SKILL.md and the folder at commit 4a72e87. 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.

    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

Single Cell Rna Analysis loads about 1.3k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 364 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
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 PKU-YuanGroup/OpenAI4S at commit 4a72e87, republished under its MIT licence (© PKU-YuanGroup). 364 words, ~1,258 tokens.

Download SKILL.mdSave it as .claude/skills/single-cell-rna-analysis/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
single-cell-rna-analysis
description
Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo DA; plus preflight validation, optional explicitly requested Harmony, checkpoints, resume, and a checksummed analysis bundle. Use for cell-called GEX matrices, not FASTQ, CITE-seq, ATAC, Multiome, spatial, trajectory, communication, or CNV analysis.
origin
openai4s
category
workflow

Single-cell RNA Analysis

Use this workflow for human or mouse 10x GEX scRNA-seq or snRNA-seq after cell calling. It preserves raw counts, keeps descriptive cluster markers separate from condition inference, and treats annotations as evidence until the user confirms them.

Before running

  1. Read the input contract, select exactly one analysis_mode, and resolve every input path. Use descriptive only for a single h5ad without a valid condition contrast; otherwise use comparative.
  2. Run preflight(config). Do not proceed when status is invalid.
  3. Show the user warnings about ambient RNA, confounding, annotation evidence, or insufficient donor replication before interpreting results.
  4. Harmony is opt-in only. Never infer a batch key or silently replace a confounded one.

Call the workflow

The directory contains hyphens, so import it with importlib:

python
import importlib

single_cell = importlib.import_module("single-cell-rna-analysis.kernel")
config = {
    "schema_version": 1,
    "analysis_mode": "comparative",
    "organism": "human",
    "modality": "scrna",
    "input": {"mode": "sample_sheet", "path": "samples.csv"},
    "reference": {
        "gene_id_type": "symbol",
        "genome_build": "GRCh38",
        "annotation_release": "GENCODE 46",
    },
    "design": {
        "tested": "stim",
        "reference": "control",
        "condition_key": "condition",
        "donor_key": "donor_id",
        "paired": True,
        "covariates": [],
    },
    "integration": {"method": "none", "batch_keys": []},
}

check = single_cell.preflight(config)
result = single_cell.run(config, "single-cell-run")

For a single h5ad with no donor or condition metadata, use descriptive mode:

python
config = {
    "schema_version": 1,
    "analysis_mode": "descriptive",
    "organism": "human",
    "modality": "scrna",
    "input": {
        "mode": "h5ad",
        "path": "pbmc3k.h5ad",
        "counts_layer": "X",
        "sample_id": "pbmc3k",
    },
    "reference": {
        "gene_id_type": "symbol",
        "genome_build": "hg19",
        "annotation_release": "GENCODE 19",
    },
    "integration": {"method": "none", "batch_keys": []},
}

Descriptive mode never invents donor/condition labels, performs integration, or emits inferential DE/DA. It runs raw-count validation, within-sample QC, embedding, resolution-sweep clustering, descriptive markers and optional evidence-assisted annotation.

run() and resume() return status, run_dir, featured_files, warnings, annotation_status, statistics_status, and manifest. Save every featured file as an Artifact:

python
for featured_file in result["featured_files"]:
    host.save_artifact(featured_file)

If a run was interrupted, call:

python
resumed = single_cell.resume("single-cell-run")

Resume validates the resolved configuration and input hashes. A changed source invalidates dependent checkpoints instead of mixing results from different inputs.

Show full SKILL.md (151 more words)Show less

Stage routing

Non-negotiable interpretation rules

  • A normalized-only matrix is not valid input for formal analysis.
  • Multiple samples do not imply that integration is appropriate.
  • UMAP appearance does not establish an optimal clustering resolution.
  • Cluster markers are descriptive and are not condition DE.
  • Descriptive mode cannot support condition, donor, treatment or causal claims.
  • Cells are not biological replicates. Inferential DE/DA requires at least three independent donors in each contrast level.
  • Candidate labels, including reference transfer, are not ground truth.
  • scVI, scGPT, GPU, remote compute, ambient correction, FASTQ processing and downstream specialty analyses require a separate, explicit workflow.

© PKU-YuanGroup, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in skills/single-cell-rna-analysis of PKU-YuanGroup/OpenAI4S.

  • SKILL.md
  • README.md
  • README_zh.md
  • kernel.py
  • references/README.md
  • references/README_zh.md
  • references/annotation-contract.md
  • references/input-contract.md
  • references/output-contract.md
  • references/scientific-workflow.md
  • references/statistics-contract.md

Open the folder on GitHubat commit 4a72e87

Compare with similar skills

Single Cell Rna Analysis 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.

Single Cell Rna Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Single Cell Rna Analysis this skillPKU-YuanGroup/OpenAI4S622—~1.3kAutomated safety check: PassMIT
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Scanpy Single-Cell Analysisdavila7/claude-code-templates33k15 repos~2.8kAutomated safety check: PassMIT
Anndatadavila7/claude-code-templates33k11 repos~2.5kAutomated safety check: PassMIT
Cellxgene Censusdavila7/claude-code-templates33k11 repos~3.8kAutomated safety check: PassMIT
Bulk RnaseqK-Dense-AI/scientific-agent-skills48k1 repos~4.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Single Cell Rna Analysis

What does Single Cell Rna Analysis do?

Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo…. Single Cell Rna Analysis is an agent skill from PKU-YuanGroup/OpenAI4S. Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo DA; plus preflight validation, optional explicitly requested Harmony, checkpoints, resume, and a checksummed analysis bundle.

When should I use Single Cell Rna Analysis?

Single Cell Rna Analysis fits situations like: cell-called GEX matrices; tasks that involve Bioinformatics.

How do I install Single Cell Rna Analysis in Claude Code?

Run `npx skills add PKU-YuanGroup/OpenAI4S --skill single-cell-rna-analysis -a claude-code`. Or copy the skill folder (skills/single-cell-rna-analysis in PKU-YuanGroup/OpenAI4S) into .claude/skills/single-cell-rna-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Single Cell Rna Analysis in Codex?

Run `npx skills add PKU-YuanGroup/OpenAI4S --skill single-cell-rna-analysis -a codex`. Or copy the skill folder (skills/single-cell-rna-analysis in PKU-YuanGroup/OpenAI4S) into .agents/skills/single-cell-rna-analysis in your project. Codex loads it when a task matches its description.

Can I use Single Cell Rna Analysis 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 PKU-YuanGroup/OpenAI4S --skill single-cell-rna-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/single-cell-rna-analysis, .gemini/skills/single-cell-rna-analysis, .github/skills/single-cell-rna-analysis and .opencode/skills/single-cell-rna-analysis in your project.

What does Single Cell Rna Analysis need to run?

Going by SKILL.md and its folder, Single Cell Rna Analysis needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Single Cell Rna Analysis 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 Single Cell Rna Analysis 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 Single Cell Rna Analysis use?

Single Cell Rna Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Single Cell Rna Analysis use?

About 1.3k tokens (SKILL.md is roughly 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 4k tokens, read only when the agent opens those files.

What are the alternatives to Single Cell Rna Analysis?

Skills that share tags, products or a category with Single Cell Rna Analysis: Single Cell Rna Qc (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Scanpy Single-Cell Analysis (davila7/claude-code-templates, 33k stars), Anndata (davila7/claude-code-templates, 33k stars) and Cellxgene Census (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Single Cell Rna Analysis?

PKU-YuanGroup (a GitHub organization) maintains it in PKU-YuanGroup/OpenAI4S, which has 622 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 9, 2026.

Source: PKU-YuanGroup/OpenAI4S on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.