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Scanpy Single-Cell Analysis

Guides an agent through scRNA-seq QC, clustering, marker ranking and plotting with Scanpy scripts

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

Once installed, the agent can run a single-cell RNA sequencing analysis in Python using the Scanpy toolkit: checking quality, normalizing counts, finding clusters and marker genes, drawing UMAP and other plots, and converting Seurat or SingleCellExperiment R objects to .h5ad. It prefers the bundled command-line scripts over writing code from scratch, and it warns about common statistical pitfalls such as confounded designs and unreviewed cell-type labels.

Great for

  • Bioinformatics researchers running exploratory scRNA-seq analysis on their own count matrices
  • Students learning a standard Scanpy workflow with templates and clear defaults
  • Data scientists who need reproducible, scripted QC-to-clustering pipelines

Skip it if

  • Clinical or diagnostic decisions: cell-type mappings and marker signatures are illustrative examples that need expert review, and results are exploratory, not biologically validated
  • Datasets that are already normalized without a declared counts layer: the scripts require identified raw counts, and guessing provenance can produce wrong results
  • Very large out-of-core datasets: the toolkit loads data into memory and backed mode is not a general out-of-core pipeline

Try asking

  • “Run QC, normalization, HVG selection, PCA, UMAP and Leiden clustering on my raw_counts.h5ad file”
  • “Find marker genes for each Leiden cluster using Wilcoxon ranking and plot a dotplot”
  • “Convert my Seurat .rds object to .h5ad and then run the Scanpy pipeline”
  • “Apply batch correction with Harmony across my samples and compare UMAPs before and after”
Step-by-step guide: how to use Scanpy Single-Cell AnalysisInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    scanpy.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip scanpy.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip scanpy.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Provide raw integer counts in a counts layer, or pass --counts-layer counts if X is already normalized, so the QC scripts accept the data
  • Install only the optional integration packages you need, since some (louvain) conflict with the tested igraph version
  • Treat cluster-to-cell-type mappings as hypotheses and check them against known markers before reporting results

What's inside

  • assets/analysis_template.py
  • assets/celltype_mapping.json
  • assets/gene_signatures.json
  • assets/pipeline_config.json
  • references/analysis_workflow.md
  • references/api_reference.md
  • references/plotting_guide.md
  • references/r_interop.md
  • references/standard_workflow.md
  • references/upstream-review.md
  • scripts/_common.py
  • scripts/annotate.py
  • scripts/batch_correct.py
  • scripts/cluster.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.