scvelo
by K-Dense-AIscvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. Use it to estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and identify driver genes. It is especially useful for scvelo for Data Analysis when you need directionality beyond standard clustering or pseudotime.
This skill scores 83/100 and is a solid directory listing candidate. It gives users a clear trigger, a specific RNA-velocity workflow, and enough operational detail to help an agent choose and use it with less guesswork than a generic prompt. Directory users should still note that it is a single-file skill without bundled scripts or extra support files, so adoption will depend on the user already working in single-cell RNA-seq/scVelo workflows.
- Clear, domain-specific trigger: RNA velocity analysis in single-cell RNA-seq, including trajectory direction, latent time, and driver genes.
- Good operational clarity: includes when-to-use guidance, concrete use cases, and an explicit install command (`pip install scvelo`).
- Trustworthy evidence: valid frontmatter, substantial body length, no placeholder markers, and cited resources/repo references.
- No scripts, rules, or support files are bundled, so the skill may require the agent to infer execution details from prose and external docs.
- Best fit is narrow: it targets scVelo-centric analysis rather than a broader single-cell workflow, so it may be less useful outside RNA velocity tasks.
Overview of scvelo skill
scvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. It helps you estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and spot driver genes. If you are doing scvelo for Data Analysis and need directionality beyond standard clustering or pseudotime, this skill is a strong fit.
What the scvelo skill is for
Use the scvelo skill when your question is about where cells are going, not just how they group. It is most useful for snapshot datasets where you want to infer developmental progression, fate branching, or lineage dynamics without a time course.
Best-fit users and projects
This skill fits researchers and analysts working in single-cell biology, especially people using Scanpy or scvi-tools. It is most valuable for RNA velocity workflows involving differentiation, state transitions, latent time ordering, and velocity-based visualization.
Why scvelo is different
Compared with a generic prompt, scvelo gives you an analysis-oriented workflow centered on RNA velocity assumptions and required inputs. That matters because success depends on preprocessing quality, spliced/unspliced layers, and dataset suitability. A good scvelo guide should help you avoid using velocity where the data cannot support it.
How to Use scvelo skill
Install and inspect the right files
Use the listed install path for the skill, then read the main skill file first. In this repository, the useful starting point is SKILL.md; there are no helper scripts or extra reference folders to follow. That means the skill body itself is the main source of workflow guidance, constraints, and usage patterns.
Give scvelo the inputs it actually needs
For useful scvelo usage, provide more than “run RNA velocity.” Include:
- dataset type and species
- whether spliced/unspliced counts are already available
- preprocessing status in Scanpy
- the analysis goal: directionality, latent time, driver genes, or fate mapping
- any known batch, sparsity, or QC issues
A stronger prompt looks like: “Analyze this pancreatic scRNA-seq AnnData object with spliced/unspliced layers, estimate RNA velocity, rank driver genes for the branching lineage, and explain which cells appear to commit to each fate.”
Follow a practical workflow
A reliable scvelo guide usually follows this order:
- Verify layers and cell/gene QC
- Normalize and filter appropriately
- Build neighbors and moments
- Estimate velocities
- Inspect velocity graph, latent time, and driver genes
- Interpret results against known biology
Do not skip the data checks. In scvelo, weak inputs often produce plausible-looking but misleading directionality.
Read the workflow sections first
If you are deciding whether the skill fits, focus on the sections that explain:
- when to use RNA velocity
- prerequisites and assumptions
- the standard workflow
- interpretation limits
Those parts tell you more than a quick skim of plots or example calls. They also help you decide whether your dataset is suitable before you spend time tuning parameters.
scvelo skill FAQ
Is scvelo only for advanced users?
No, but it is not beginner-proof. If you already work in Scanpy or single-cell workflows, scvelo is approachable. Beginners can use it, but only if they understand AnnData structure, count layers, and basic QC.
How is scvelo different from a normal prompt?
A normal prompt can describe RNA velocity conceptually, but the scvelo skill is better for analysis execution. It is centered on the actual workflow, required inputs, and interpretation steps that affect whether the result is credible.
When should I not use scvelo?
Do not use scvelo if you lack unspliced/spliced information, have very shallow data, or only need a broad clustering summary. If your dataset cannot support velocity assumptions, a pseudotime or differential expression analysis may be a better choice.
Does scvelo replace Scanpy or scvi-tools?
No. The scvelo skill complements them. In practice, you often use Scanpy for preprocessing and visualization, then scvelo for velocity-specific inference and latent-time interpretation.
How to Improve scvelo skill
Start with a biologically specific question
The best scvelo results come from a clear target: fate branch, direction of differentiation, driver genes, or latent ordering. “Analyze this dataset” is too vague. “Identify the likely transition path from progenitor to two terminal states” gives the model a much better objective.
Provide preprocessing and quality context
The biggest failure mode in scvelo is weak or missing preprocessing detail. Tell the skill whether filtering, normalization, highly variable gene selection, and neighbor graph construction have already been done. Also mention obvious issues like sparse counts, mixed cell states, or batch effects.
Ask for interpretation, not just code
Useful outputs should explain what the velocity results mean biologically. Ask for the main transitions, the confidence limits, and which genes support the inferred direction. That makes the scvelo skill more useful for decision-making, not just plotting.
Iterate with one concrete output at a time
If the first result is too broad, narrow it. For example, ask next for:
- the top velocity driver genes in one lineage
- a comparison of latent time across clusters
- a check for whether the inferred direction matches known markers
This is the fastest way to improve scvelo for Data Analysis without overloading the workflow.