Agent skill

Scrna Orchestrator

Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from…

MITAuto-check passedResearch & Science

Install Scrna Orchestrator

skills CLI

$ npx skills add ClawBio/ClawBio --skill scrna-orchestrator -a claude-code

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

GitHub CLI

$ gh skill install ClawBio/ClawBio scrna-orchestrator --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/ClawBio/ClawBio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scrna-orchestrator .claude/skills/scrna-orchestrator && 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 "scrna-orchestrator" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator into .claude/skills/scrna-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-orchestrator", 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/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator

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 ClawBio/ClawBio --skill scrna-orchestrator -a codex

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

GitHub CLI

$ gh skill install ClawBio/ClawBio scrna-orchestrator --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/scrna-orchestrator .agents/skills/scrna-orchestrator && 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 "scrna-orchestrator" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator into .agents/skills/scrna-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-orchestrator", 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 ClawBio/ClawBio --skill scrna-orchestrator -a cursor

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

GitHub CLI

$ gh skill install ClawBio/ClawBio scrna-orchestrator --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/scrna-orchestrator .cursor/skills/scrna-orchestrator && 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 "scrna-orchestrator" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator into .cursor/skills/scrna-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-orchestrator", 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/ClawBio/ClawBio.git --path skills/scrna-orchestrator

--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 ClawBio/ClawBio --skill scrna-orchestrator -a gemini-cli

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

GitHub CLI

$ gh skill install ClawBio/ClawBio scrna-orchestrator --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/ClawBio/ClawBio.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/scrna-orchestrator .gemini/skills/scrna-orchestrator && 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 "scrna-orchestrator" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator into .gemini/skills/scrna-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-orchestrator", 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 ClawBio/ClawBio scrna-orchestrator

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 ClawBio/ClawBio --skill scrna-orchestrator -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/scrna-orchestrator .github/skills/scrna-orchestrator && 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 "scrna-orchestrator" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator into .github/skills/scrna-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-orchestrator", 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 ClawBio/ClawBio --skill scrna-orchestrator -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 ClawBio/ClawBio scrna-orchestrator --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/scrna-orchestrator .opencode/skills/scrna-orchestrator && 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 "scrna-orchestrator" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator into .opencode/skills/scrna-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-orchestrator", 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: scrna-orchestrator
GitHub stars: 1.2k
Used in: 1 other repo
Token cost: ~2.9k tokens
SKILL.md length: 846 words
Files: 3
Skills in repo: 104
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from…

Works in 9 steps: QC and Filtering: Mitochondrial… → Optional Doublet Detection: Scrublet on… → Preprocessing: Library-size… → …
Tasks that involve Bioinformatics
SKILL.md covers Why This Exists, Core Capabilities, Input Formats and Workflow, plus 10 more sections
Runs Python scripts from its folder; calls python

What it does

Scrna Orchestrator is an agent skill from ClawBio/ClawBio. Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from integrated.h5ad/Xscvi, and optional dataset-level plus within-cluster contrastive marker analysis from raw-count .h5ad or 10x Matrix Market input.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `scrna_orchestrator.py` and `tests/test_scrna_orchestrator.py`).

It sits in Research & Science, covering Bioinformatics. It works with Scanpy. The repository describes itself as: 🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free. The licence is MIT.

When your agent uses it

Tasks that involve Bioinformatics

Example prompts

“/scrna-orchestrator”

Requirements

Python 3

Workflow steps

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

QC and Filtering: Mitochondrial percentage filtering and min genes/cells thresholds.
Optional Doublet Detection: Scrublet on QC-filtered raw counts before downstream analysis.
Preprocessing: Library-size normalization, log1p, and HVG selection.
Embedding and Clustering: PCA or latent-representation neighbors graph, UMAP, Leiden clustering.
Cluster Markers: Wilcoxon cluster-vs-rest marker detection on normalized full-gene expression.
Optional Cell Type Annotation: Local-only CellTypist annotation aggregated to cluster-level putative labels.
Optional Dataset-Level Contrasts: All-pairs Wilcoxon contrastive marker analysis across the observed values of any obs column.
Optional Within-Cluster Contrasts: All-pairs Wilcoxon contrastive marker analysis inside each Leiden cluster or another chosen partition…
Reporting: Markdown report, CSV/TSV tables, PNG figures, and reproducibility files.

What it can do on your machine

Read from SKILL.md and the folder at commit dece754. 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
Links to these hosts (documentation or services it may open):
- scanpy.readthedocs.io
- anndata.readthedocs.io
- nature.com
- genomebiology.biomedcentral.com
- celltypist.org
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

Scrna Orchestrator loads about 2.9k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 846 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~86
When it runs · the whole SKILL.md, loaded when a task matches: ~2.9k

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 ClawBio/ClawBio at commit dece754, republished under its MIT licence (© ClawBio). 846 words, ~2,869 tokens.

Download SKILL.mdSave it as .claude/skills/scrna-orchestrator/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.

name: scrna-orchestrator
description: Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from integrated.h5ad/X_scvi, and optional dataset-level plus within-cluster contrastive marker analysis from raw-count .h5ad or 10x Matrix Market input.
license: MIT
metadata.version: 0.1.0
metadata.author: Yonghao Zhao
metadata.tags: scrna, single-cell, scanpy, clustering, differential-expression, h5ad, mtx, 10x

🦖 scRNA Orchestrator

You are scRNA Orchestrator, a specialised ClawBio agent for local single-cell RNA-seq analysis with Scanpy.

Why This Exists

Single-cell workflows are easy to misconfigure and hard to reproduce when run ad hoc.

Without it: Users manually stitch QC, normalization, clustering, marker analysis, and latent downstream interpretation with inconsistent defaults.
With it: One command produces a consistent report.md, figures, tables, structured metadata, and a reproducibility bundle, whether the graph is built from PCA or X_scvi.
Why ClawBio: The workflow is local-first, explicit about assumptions (raw counts), and ships machine-readable outputs.

Core Capabilities

QC and Filtering: Mitochondrial percentage filtering and min genes/cells thresholds.
Optional Doublet Detection: Scrublet on QC-filtered raw counts before downstream analysis.
Preprocessing: Library-size normalization, log1p, and HVG selection.
Embedding and Clustering: PCA or latent-representation neighbors graph, UMAP, Leiden clustering.
Cluster Markers: Wilcoxon cluster-vs-rest marker detection on normalized full-gene expression.
Optional Cell Type Annotation: Local-only CellTypist annotation aggregated to cluster-level putative labels.
Optional Dataset-Level Contrasts: All-pairs Wilcoxon contrastive marker analysis across the observed values of any obs column.
Optional Within-Cluster Contrasts: All-pairs Wilcoxon contrastive marker analysis inside each Leiden cluster or another chosen partition column.
Reporting: Markdown report, CSV/TSV tables, PNG figures, and reproducibility files.

Input Formats

Format	Extension	Required Fields	Example
AnnData raw counts or latent downstream artifact	`.h5ad`	Raw count matrix in `X` or recoverable raw counts in `layers["counts"]`; optional latent rep in `obsm["X_scvi"]`; cell metadata in `obs`; gene metadata in `var`	`pbmc_raw.h5ad`, `integrated.h5ad`
10x Matrix Market	directory, `.mtx`, `.mtx.gz`	`matrix.mtx(.gz)` plus matching `barcodes.tsv(.gz)` and `features.tsv(.gz)` or `genes.tsv(.gz)`	`filtered_feature_bc_matrix/`
Demo mode	n/a	none	`python clawbio.py run scrna --demo`

Notes:

Processed/normalized/scaled .h5ad inputs are rejected unless they are a recoverable latent downstream artifact with raw counts preserved in layers["counts"].
10x input can be passed as the containing directory or directly as matrix.mtx(.gz).
pbmc3k_processed-style inputs are out of scope for this skill.

Workflow

When the user asks for scRNA QC/clustering/markers/annotation/contrastive markers:

Validate: Check raw-count .h5ad or 10x Matrix Market input (or --demo), and reject processed-like matrices.
Filter: Run QC filtering, and optionally remove predicted doublets with Scrublet.
Process: Normalize, log1p, select HVGs, and build the graph from PCA or a latent rep such as X_scvi.
Analyze:

Always run cluster marker analysis (leiden, Wilcoxon).
Optionally run CellTypist on the normalized full-gene matrix.
Optionally run dataset-level contrasts, within-cluster contrasts, or both when --contrast-groupby is provided.

Generate: Write report.md, result.json, tables, figures, and reproducibility bundle.

CLI Reference

bash

# Standard usage
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir>

# 10x Matrix Market directory
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <filtered_feature_bc_matrix_dir> --output <report_dir>

# Direct matrix.mtx(.gz) path
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <matrix.mtx.gz> --output <report_dir>


# Demo mode
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --demo --output <report_dir>

# Optional doublet detection
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --doublet-method scrublet

# Optional CellTypist annotation
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --annotate celltypist --annotation-model Immune_All_Low

# Optional dataset-level pairwise contrasts
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --contrast-groupby <obs_column> --contrast-scope dataset

# Optional dataset-level + within-cluster contrasts together
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --contrast-groupby <obs_column> --contrast-scope both \
  --contrast-clusterby leiden

# Optional latent downstream mode
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <integrated.h5ad> --output <report_dir> \
  --use-rep X_scvi

# Via ClawBio runner
python clawbio.py run scrna --input <input.h5ad> --output <report_dir>
python clawbio.py run scrna --input <filtered_feature_bc_matrix_dir> --output <report_dir>
python clawbio.py run scrna --demo

Demo

bash

python clawbio.py run scrna --demo
python clawbio.py run scrna --demo --doublet-method scrublet

Expected output:

report.md with QC, clustering, markers, and optional annotation/contrast summaries
figure files (qc_violin.png, umap_leiden.png, marker_dotplot.png)
marker, doublet, annotation, dataset-level contrast, and within-cluster contrast tables when enabled
reproducibility bundle

Show full SKILL.md (406 more words)Show less

Algorithm / Methodology

Compute QC metrics (n_genes_by_counts, total_counts, pct_counts_mt)
Filter by min_genes, min_cells, max_mt_pct

Optional doublet detection:

scanpy.pp.scrublet on QC-filtered raw counts
Remove predicted doublets before normalization and clustering

Preprocess:

Normalize total counts to 1e4
Apply log1p
Select HVGs (flavor="seurat")

Embed and cluster:

Scale (max_value=10) on the HVG branch
PCA, neighbors graph, UMAP
Leiden clustering

Markers:

scanpy.tl.rank_genes_groups(groupby="leiden", method="wilcoxon", pts=True)

Optional annotation:

Run local CellTypist on normalized/log1p full-gene expression
Aggregate per-cell predictions to cluster-level majority labels with support and confidence

Optional dataset-level contrasts:

For every unordered pair of observed groups in --contrast-groupby, run scanpy.tl.rank_genes_groups(..., groups=[group1], reference=group2, method="wilcoxon", pts=True)
Export full statistics and top genes by score per pairwise comparison

Optional within-cluster contrasts:

For every cluster in --contrast-clusterby and every unordered pair of observed groups in --contrast-groupby, run the same Wilcoxon contrast on the cluster subset
Skip cluster/comparison pairs where either side has fewer than 2 cells, and report the skipped count

Example Queries

"Run standard QC and clustering on my h5ad file"
"Cluster my 10x matrix.mtx directory"
"Find marker genes for each cluster"
"Generate a UMAP coloured by cluster"
"Remove predicted doublets before clustering"
"Assign putative CellTypist labels to clusters"
"Run all pairwise contrastive markers for treated vs control vs rescue"
"Find within-cluster treatment markers in each Leiden cluster"

Output Structure

text

output_directory/
├── report.md
├── result.json
├── figures/
│   ├── qc_violin.png
│   ├── umap_leiden.png
│   └── marker_dotplot.png
├── tables/
│   ├── cluster_summary.csv
│   ├── markers_top.csv
│   ├── markers_top.tsv
│   ├── doublet_summary.csv      # only when doublet detection is enabled
│   ├── cluster_annotations.csv  # only when annotation is enabled
│   ├── contrastive_markers_full.csv              # only when dataset-level contrasts are enabled
│   ├── contrastive_markers_top.csv               # only when dataset-level contrasts are enabled
│   ├── within_cluster_contrastive_markers_full.csv  # only when within-cluster contrasts are enabled
│   └── within_cluster_contrastive_markers_top.csv   # only when within-cluster contrasts are enabled
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Dependencies

Required:

scanpy >= 1.10
anndata >= 0.10
scipy
numpy, pandas, matplotlib, leidenalg, python-igraph

Optional:

scrublet for --doublet-method scrublet
celltypist for --annotate celltypist

Out of scope:

scvi-tools / scANVI

Safety

Local-first: No patient data upload.
Disclaimer: Reports include the ClawBio medical disclaimer.
Input guardrails: Rejects processed-like matrices to reduce invalid biological inferences.
Annotation caution: CellTypist labels are putative and model-dependent, not definitive biology.
Model downloads: Runtime CellTypist model downloads are intentionally disabled.
Reproducibility: Writes command/environment/checksum bundle.

Integration with Bio Orchestrator

Trigger conditions:

File extension .h5ad, .mtx, or .mtx.gz
User intent includes scRNA terms (single-cell, Scanpy, clustering, marker genes, contrastive markers, doublets, annotation)

Current limitations:

Raw-count .h5ad and 10x Matrix Market only
CellTypist support is human-model focused and requires a locally installed model

Status

MVP implemented -- supports .h5ad and 10x Matrix Market input, PBMC3k-first demo data (fallback to synthetic on failure), opt-in Scrublet doublet detection, opt-in local CellTypist annotation, opt-in latent downstream mode from integrated.h5ad, and opt-in dataset-level plus within-cluster pairwise contrastive markers.

Citations

Scanpy documentation — analysis API and methods.
AnnData documentation — data model.
Leiden algorithm paper — community detection.
Scrublet paper — computational doublet detection.
CellTypist documentation — model-based immune and general cell annotation.

Files

SKILL.md and 2 other files in skills/scrna-orchestrator of ClawBio/ClawBio.

SKILL.md
scrna_orchestrator.py
tests/test_scrna_orchestrator.py

Open the folder on GitHubat commit dece754

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in ClawBio/ClawBio, which our catalogue first saw on October 7, 2026.

BioTender-max/awesome-bio-agent-skillsskills/clawbio/scrna-orchestrator

Compare with similar skills

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Questions about Scrna Orchestrator

What does Scrna Orchestrator do?

Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from…. Scrna Orchestrator is an agent skill from ClawBio/ClawBio.h5ad or 10x Matrix Market input.

When should I use Scrna Orchestrator?

Scrna Orchestrator fits situations like: tasks that involve Bioinformatics.

How do I install Scrna Orchestrator in Claude Code?

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

How do I install Scrna Orchestrator in Codex?

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

Can I use Scrna Orchestrator 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 ClawBio/ClawBio --skill scrna-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scrna-orchestrator, .gemini/skills/scrna-orchestrator, .github/skills/scrna-orchestrator and .opencode/skills/scrna-orchestrator in your project.

What does Scrna Orchestrator need to run?

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

Does Scrna Orchestrator access the network?

SKILL.md names 5 domains. As links in the text: scanpy.readthedocs.io, anndata.readthedocs.io, nature.com, genomebiology.biomedcentral.com and celltypist.org. This is read from the text; nothing was executed.

Is Scrna Orchestrator 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 Scrna Orchestrator use?

Scrna Orchestrator 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 Scrna Orchestrator use?

About 2.9k tokens (SKILL.md is roughly 11k 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 Scrna Orchestrator?

Skills that share tags, products or a category with Scrna Orchestrator: Single Cell Rna Qc (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Scanpy Single-Cell Analysis (davila7/claude-code-templates, 33k stars), Single Cell Rna Analysis (PKU-YuanGroup/OpenAI4S, 622 stars) and Anndata (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 Scrna Orchestrator?

ClawBio (a GitHub organization) maintains it in ClawBio/ClawBio, which has 1,155 GitHub stars. The repository holds 104 skills in this directory. The repository was last updated on October 9, 2026.

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

Install Scrna Orchestrator

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SKILL.md

🦖 scRNA Orchestrator

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