kdense-scvi-tools

Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.

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Repository: eightmm/codex-science
Last source activity: July 10, 2026 at 05:18
Detected SKILL.md language: English
Stars: 2
Forks: 0

Install options

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Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

A direct command skips the review prompt. Inspect the source before running it.

npx skills add https://github.com/eightmm/codex-science --skill kdense-scvi-tools

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Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.

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SKILL.md

Source instructions · Read-only preview

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Kdense Scvi Tools

Use this Codex adapter for the pinned upstream scientific skill.

Catalog status

Status: inactive

Source: kdense

Audit reasons: credentials-required

Upstream license: BSD-3-Clause license

Inventory: ../../inventory.json

Upstream instructions: ../../../vendor/scientific-agent-skills/skills/scvi-tools/SKILL.md

Procedure

Resolve the plugin root as three directories above this skill directory.

Read the matching inventory record and verify its status, reasons, source path, and pinned revision.

Do not read the upstream instructions until the user has seen every audit reason and given explicit acknowledgement. Acknowledgement permits instruction inspection only.

Treat upstream frontmatter as source metadata, not as Codex configuration. Follow the instruction body only where it is compatible with available Codex tools and the current user-approved plan.

Treat tools named for another agent runtime as capability requests. Map them to an available Codex tool only when the behavior is equivalent; otherwise report the unavailable capability.

Ask before credentials, package installation, new network hosts, remote compute, writes, paid services, destructive actions, or imported executable code.

Use $science-provenance for produced scientific outputs and $science-review before presenting scientific claims.

Boundaries

This wrapper does not grant credentials, network access, package installation, or script execution.

Do not change an inactive record to active from inside a research task.

Preserve failures, uncertainty, source identifiers, and licensing constraints.

name	kdense-scvi-tools
description	Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.