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 agentlas-ai/agentlas-desktop --skill scvi-toolsgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/scvi-tools)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/scvi-tools"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/scvi-tools/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/agentlas-ai/agentlas-desktop/scvi-tools"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/scvi-tools.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 | $0.00065 | $0.02263 |
| Opus 5.5 | $0.00026 | $0.00905 |
| Sonnet 5.5 | $0.00013 | $0.00453 |
| Haiku 4.5 | $0.00006 | $0.00226 |
Grade A, and why
scvi-tools 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 1mo 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
100% identical to scvi-tools — 289 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scvi-tools
Overview
scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities. Current stable release: scvi-tools 1.4.3 (May 2026).
Model namespaces matter: core models (scVI, scANVI, totalVI, MultiVI, PeakVI, AUTOZI, CondSCVI, DestVI, LinearSCVI, AmortizedLDA, JaxSCVI) live under scvi.model. Most other models (VeloVI, contrastiveVI, CellAssign, PoissonVI, scBasset, MrVI, MethylVI/MethylANVI, CytoVI, SysVI, Decipher, gimVI, scVIVA, ResolVI, Stereoscope, Solo, totalANVI, DIAGVI) live under scvi.external. The reference files specify the correct namespace per model.
When to Use This Skill
Use this skill when:
- Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)
- Working with single-cell ATAC-seq or chromatin accessibility data
- Integrating multimodal data (CITE-seq, multiome, paired/unpaired datasets)
- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)
- Performing differential expression analysis on single-cell data
- Conducting cell type annotation or transfer learning tasks
- Working with specialized single-cell modalities (methylation, cytometry, RNA velocity)
- Building custom probabilistic models for single-cell analysis
Core Capabilities
scvi-tools provides models organized by data modality:
1. Single-Cell RNA-seq Analysis
Core models for expression analysis, batch correction, and integration. See references/models-scrna-seq.md for:
- scVI: Unsupervised dimensionality reduction and batch correction
- scANVI: Semi-supervised cell type annotation and integration
- AUTOZI: Zero-inflation detection and modeling
- VeloVI: RNA velocity analysis
- contrastiveVI: Perturbation effect isolation
2. Chromatin Accessibility (ATAC-seq)
Models for analyzing single-cell chromatin data. See references/models-atac-seq.md for:
- PeakVI: Peak-based ATAC-seq analysis and integration
- PoissonVI: Quantitative fragment count modeling
- scBasset: Deep learning approach with motif analysis
What ships with it
8 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.
- references/differential-expression.md 14 KB
- references/models-atac-seq.md 9.8 KB
- references/models-multimodal.md 12 KB
- references/models-scrna-seq.md 10 KB
- references/models-spatial.md 12 KB
- references/models-specialized.md 10.0 KB
- references/theoretical-foundations.md 11 KB
- references/workflows.md 13 KB
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.
- 1mo ago First seen · 218 lines · 65 tokens per session scan A 8426ebdb26da
scvi-tools is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 2,263 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to scvi-tools, differing in 289 lines, and is treated as a copy.
Other skills, from other repositories
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
pick-a-pii-model
Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…
reactome-database
Query the Reactome database (Analysis and Content Services). Use when the user asks about pathway analysis, gene list enrichment, retrieving results by token, finding unmapped or not-found identifiers, mapping identifiers, reaction participants (inputs, outputs), pathway hierarchy (including top-level pathways)…
borzoi
Use Borzoi-style regulatory genomics models for sequence-to-expression or variant-effect analysis. Use when the task asks for noncoding variant impact, regulatory sequence design, or expression prediction.