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How to run a single-cell RNA-seq analysis with Scanpy with Scientific Agent Skills

Have an agent follow the Scanpy skill to process a single-cell dataset and report clusters.

  • Research
  • Advanced
  • 1 hour
  • From the README
  • Claude Code
  • Codex
  • Cursor

Who it is forA bench biologist or bioinformatics staff member with a single-cell RNA-seq dataset and no time to write the pipeline from scratch.

The problemWriting a single-cell pipeline from scratch takes days of package reading and debugging.

The outcomeA reproducible notebook or script that loads the data, filters cells, clusters them, and reports the main groups found.

What you need first

  • An agent host that supports Agent Skills, such as Claude Code, Cursor, or Codex
  • Git, to clone the repository (inferred)
  • Python, since the repository language is Python
  • API keys or accounts for any registered or licensed database you plan to query

Steps

  1. git clone https://github.com/K-Dense-AI/scientific-agent-skills (inferred, check the README)
  2. Follow the install steps in the Getting Started section of the README for your agent host.
  3. Locate the single-cell and Scanpy skill folder under skills/.
  4. pip install scanpy (inferred, check the README)
  5. Give your agent the dataset path and ask for quality control, clustering, and a cluster summary.

Prompt for your agent

Using the scientific-agent-skills repo and its Scanpy single-cell skill, process the dataset at <dataset path> with quality control, clustering, and a cluster summary. Return a script and a short report of cell counts per cluster. Verify the cell and gene counts before and after filtering.

Caveat. The README names Scanpy among the package skills but does not specify input formats, so confirm your file type before the run.

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