Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scvelogit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scvelo)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scvelo"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scvelo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scvelo"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scvelo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00130 | $0.03041 |
| Opus 5 | $0.00065 | $0.01520 |
| Sonnet 5 | $0.00026 | $0.00608 |
| Haiku 4.5 | $0.00013 | $0.00304 |
Grade A, and why
alterlab-scvelo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to scvelo — 40 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scVelo — RNA Velocity Analysis
Overview
scVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decisions without requiring time-course data.
Installation: uv pip install "scvelo==0.3.4" (latest as of mid-2025). Gotcha: scVelo's deps declare numpy>=1.17 with no upper bound, but the stack breaks under numpy 2.x — if you hit cryptic np.float_/dtype errors on import or in plotting, pin numpy<2 (e.g. numpy==1.26.4). pandas 2.x is fine on 0.3.x.
Key resources:
- Documentation: https://scvelo.readthedocs.io/
- GitHub: https://github.com/theislab/scvelo
- Paper: Bergen et al. (2020) Nature Biotechnology. PMID: 32747759
When to Use This Skill
Use scVelo when:
- Trajectory inference from snapshot data: Determine which direction cells are differentiating
- Cell fate prediction: Identify progenitor cells and their downstream fates
- Driver gene identification: Find genes whose dynamics best explain observed trajectories
- Developmental biology: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions
- Latent time estimation: Order cells along a pseudotime derived from splicing dynamics
- Complement to Scanpy: Add directional information to UMAP embeddings
Prerequisites
scVelo requires count matrices for both unspliced and spliced RNA. These are generated by:
- STARsolo or kallisto|bustools with
lamannomode - velocyto CLI:
velocyto run10x/velocyto run - alevin-fry / simpleaf with spliced/unspliced output
Data is stored in an AnnData object with layers["spliced"] and layers["unspliced"].
Standard RNA Velocity Workflow
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 327 lines · 130 tokens per session scan A aaafe7df7029
alterlab-scvelo is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 7d ago), licensed MIT. It adds 130 tokens to every session and 3,041 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to scvelo, differing in 40 lines, and is treated as a copy.
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