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scVelo RNA Velocity Analysis

Runs RNA velocity analysis on single-cell spliced/unspliced counts to infer cell-state direction

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

What it does

This skill teaches an AI agent to run RNA velocity analysis with the scVelo library on single-cell sequencing data. RNA velocity uses the ratio of newly made (unspliced) to mature (spliced) RNA to estimate which direction cells are likely moving in gene-expression space. The agent can fit deterministic or dynamical models, inspect individual genes, build velocity graphs, estimate latent time, and rank genes linked to velocity, and it is told to treat results as hypotheses rather than proof of cell fate.

Great for

  • Bioinformaticians analysing single-cell RNA-seq data who want directional trajectory hypotheses
  • Researchers who already have spliced and unspliced count matrices from a velocity-aware quantifier
  • Analysts working alongside Scanpy who need kinetic-model diagnostics

Skip it if

  • Users without aligned spliced/unspliced count data, since the workflow cannot run without them
  • Anyone expecting velocity arrows to prove lineage, causal drivers or future cell fate, which the skill itself warns against
  • Stochastic-model analyses on the current stack, because the default solver fails with NumPy 2 and needs a separately validated legacy environment

Try asking

  • “Run a dynamical RNA velocity analysis on my velocity_counts.h5ad grouped by clusters and save the results”
  • “Import my loom file, align it to my annotated H5AD, and run a deterministic velocity check”
  • “Rank the velocity-associated genes for each cell type and show phase portraits for the top ones”
  • “Estimate latent time and velocity pseudotime for my differentiation dataset”
Step-by-step guide: how to use scVelo RNA Velocity AnalysisInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    scvelo.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

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

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip scvelo.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

  • Supply raw, non-logged, non-batch-corrected counts in the spliced and unspliced layers, and keep the original full-gene file, since the workflow filters genes and transforms data.
  • Pass a copy of your AnnData object to the script, because it modifies the data in place.
  • Keep the pandas 2.3.3 pin, since pandas 3 breaks scVelo 0.3.4 dynamical fitting and some plots.

What's inside

  • references/velocity_models.md
  • scripts/rna_velocity_workflow.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.