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 GGbond-bo/MemOmics-Agent --skill create_scvi_embeddings_scrnagit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote 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/ggbond-bo/memomics-agent/create_scvi_embeddings_scrna)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/create_scvi_embeddings_scrna"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/create_scvi_embeddings_scrna/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/ggbond-bo/memomics-agent/create_scvi_embeddings_scrna"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/create_scvi_embeddings_scrna.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.02409 |
| Opus 5 | $0.00016 | $0.01205 |
| Sonnet 5 | $0.00006 | $0.00482 |
| Haiku 4.5 | $0.00003 | $0.00241 |
Grade A, and why
create_scvi_embeddings_scRNA 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Scvi Embeddings Scrna
Create scVI and scANVI embeddings for single-cell RNA-seq data, saving the results to an AnnData object.
When to Use
When you need create scvi embeddings scRNA analysis
Parameters
| Parameter | Default | Notes |
|---|---|---|
adata_filename |
[Required] Filename of the AnnData object to load (str) | |
batch_key |
[Required] Column name in adata.obs for batch information (str) | |
label_key |
[Required] Column name in adata.obs for cell type labels (str) | |
data_dir |
[Required] Directory path where the AnnData file is located and where output will be saved (str) |
⚠️ batch_key 预检查（写代码前必须执行）：使用
batch_key前必须先用adata.obs['<batch_key>'].nunique()检查唯一条目数。若 >100 且 ≠ 预期样本数 → 阻断执行，提示用户可能误用了 barcode/cells 列。已有sample_id (16,003 unique)的前车之鉴。
Parameter Adaptation: Adjust parameters based on tissue quality, species, and condition. Literature values take priority, then official defaults, then tissue-specific adjustments.
Proven Scripts
Scripts that have been successfully executed and passed analysis review. These are automatically saved after successful runs.
| Species | Tissue | Condition | Date | Score |
|---|---|---|---|---|
| (none yet) |
Common Issues
| Error | Cause | Solution |
|---|---|---|
| (accumulated from runs) |
References
- Source: Biomni
- Category: genomics
- Language: Python
📊 集成质量评估（必输出）
铁轨规则：scVI 嵌入后，必须运行 4 项评估并输出图表。未输出 → rail_review(post) 阻断。
必输出指标（4 项铁轨）
| # | 指标 | 通过 | 警告 | 阻断 |
|---|---|---|---|---|
| 1 | LISI | > N_batch×0.8 | 0.5-0.8 | < 0.5 |
| 2 | ASW(batch) | < 0.1 | 0.1-0.15 | > 0.15 |
| 3 | kBET | rejection < 0.05 | 0.05-0.15 | > 0.15 |
| 4 | ELBO 收敛 | loss 平稳 | loss 波动 < 10% | loss 未收敛 |
scVI 额外检查
- latent 维度：
adata.obsm['X_scVI'].shape[1]≤ 30，过大 → 过拟合 - 重构误差：NMSE 应在 0.1-0.5
What ships with it
2 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.
- 9d ago First seen · 193 lines · 32 tokens per session scan A 8f052c889db9
create_scvi_embeddings_scRNA is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 2,409 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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