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 annotate_celltype_with_panhumanpygit 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/annotate_celltype_with_panhumanpy)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/annotate_celltype_with_panhumanpy"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/annotate_celltype_with_panhumanpy/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/annotate_celltype_with_panhumanpy"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/annotate_celltype_with_panhumanpy.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 | $0.00050 | $0.02232 |
| Opus 5 | $0.00025 | $0.01116 |
| Sonnet 5 | $0.00010 | $0.00446 |
| Haiku 4.5 | $0.00005 | $0.00223 |
Grade A, and why
annotate_celltype_with_panhumanpy 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Annotate Celltype With Panhumanpy
Perform cell type annotation of single-cell RNA-seq data using Panhuman Azimuth Neural Network. This function implements the Panhuman Azimuth workflow for cell type annotation using the panhumanpy package, providing hierarchical cell type labels for tissues across the human body.
When to Use
When you need annotate celltype with panhumanpy analysis
Parameters
| Parameter | Default | Notes |
|---|---|---|
adata_path |
[Required] Path to the AnnData file containing scRNA-seq data (str) | |
feature_names_col |
[Optional] Column name in adata.var containing gene symbols (default: None, uses index) | |
refine |
[Optional] Whether to perform additional label refinement for consistent granularity (default: True) | |
umap |
[Optional] Whether to generate ANN embeddings and UMAP (default: True) | |
output_dir |
[Optional] Directory to save results (default: ./output) |
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
🗣️ 辩论机制（debate_analysis）
本 skill 在执行后，如果涉及参数选择、方法决策、结果判断等不确定环节，必须调用 工具进行多角色辩论。
辩论规则
- 正方 3 位专业编辑（各自独立，互相看不到）：生物学编辑 / 统计学编辑 / 生信编辑
- 反方 4 位专业编辑（各自独立，互相看不到，也看不到正方）：生物学编辑 / 统计学编辑 / 生信编辑 / 历史经验编辑
- 裁判：看到所有 7 方论点后给出裁决 + 置信度（高/中/低）
- 上下文隔离：每个编辑是独立的 LLM API 调用，messages 只包含自己的 prompt
触发场景
- 参数选择有多个合理选项时（如分辨率 0.4 vs 0.6 vs 0.8）
- 结果可能受方法选择影响时（如不同注释方法给出不同结果）
- 生物结论需要验证可靠性时
- QC 阈值不确定时（如 MT% 阈值 10% vs 15% vs 20%）
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 · 163 lines · 50 tokens per session scan A 53db1862d5b7
annotate_celltype_with_panhumanpy is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 50 tokens to every session and 2,232 once invoked, about $0.0003 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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