Scvelo

If you're analyzing single-cell RNA-seq and need to figure out which direction cells are differentiating without time-course data, this is the standard tool. It uses the ratio of unspliced to spliced mRNA to infer velocity vectors, letting you overlay directional arrows on your UMAP plots. The dynamical mode is more accurate than stochastic but takes 10-30 minutes for 10K cells. You'll need preprocessed counts from STARsolo or velocyto with both spliced and unspliced layers. Works seamlessly with Scanpy AnnData objects and gives you latent time, driver genes, and PAGA-compatible trajectory graphs. The phase portraits are genuinely useful for validating that your velocity estimates make biological sense.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill scvelo --agent claude-code

Installs into .claude/skills of the current project.

Files

scVelo RNA Velocity

When to use

Use for kinetic analysis of aligned spliced/unspliced RNA counts, directional trajectory hypotheses, gene-level phase portraits, and dynamical latent time. Use the model reference for assumptions, transition probabilities, and an optional CellRank handoff.

RNA velocity estimates an expression derivative under a model. Smooth arrows are not observed cell movement, lineage tracing, causal drivers, or proof of future fate. The dynamical estimator is not automatically more accurate than a steady-state estimator on every dataset.

Tested environment and compatibility

The maintained workflow targets scVelo 0.3.4, Scanpy 1.12.4, AnnData 0.13.4, NumPy 2.5.3, pandas 2.3.3, SciPy 1.18.1, Matplotlib 3.11.2, and loompy 3.0.8 on Python 3.13. Use an isolated environment:

uv venv --python 3.13 .venv-velocity
uv pip install --python .venv-velocity/bin/python \
  scvelo==0.3.4 scanpy==1.12.4 anndata==0.13.4 numpy==2.5.3 \
  pandas==2.3.3 scipy==1.18.1 matplotlib==3.11.2 loompy==3.0.8

SKILL.md on GitHub