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 doubletfinder-remove-doubletsgit 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/doubletfinder-remove-doublets)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/doubletfinder-remove-doublets"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/doubletfinder-remove-doublets/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/doubletfinder-remove-doublets"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/doubletfinder-remove-doublets.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 | $0.00029 | $0.02452 |
| Opus 5 | $0.00015 | $0.01226 |
| Sonnet 5 | $0.00006 | $0.00490 |
| Haiku 4.5 | $0.00003 | $0.00245 |
Grade A, and why
doubletfinder-remove-doublets 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DoubletFinder 去双胞
DoubletFinder通过模拟人工双胞在PC空间中检测doublets。参数自适应: pK通过BCparamOpt优化; 衰老样本→预期双胞数+20%; 免疫细胞丰富→+10%。所有10x数据都应做双胞检测
When to Use
scRNA-seq数据需去除双细胞
Triggers
DoubletFinder去双胞doublet双细胞
Pipeline
- Seurat PCA预处理
- BCparamOpt优化pK
- 计算nExp_poi预期双胞数
- doubletFinder检测
- UMAP标注双胞
- subset过滤双胞
- 保存到doubletfinder/目录
Parameters
| Parameter | Default | Notes |
|---|---|---|
pN |
0.25 | |
pK |
auto(BCparamOpt) | |
nExp_poi |
auto(细胞数0.008千细胞数) |
Parameter Adaptation: Adjust parameters based on tissue quality, species, and condition. Literature values take priority, then official defaults, then tissue-specific adjustments.
Dependencies
DoubletFinderSeuratspatstat
Outputs
- 双胞标注对象
- pK优化曲线
- UMAP双胞图
- 过滤后对象
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-style (custom)
- Category: decontamination
- Language: R
🗣️ 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%）
不触发场景
- 参数有明确知识库推荐且无争议时
- 纯计算步骤（如保存文件、读取数据）
🔒 审查机制（rail_review）
本 skill 执行代码前必须调用 进行前置审查，执行后必须调用 进行后置审查。
审查内容
- pre 审查：环境检查（包是否安装）→ 参数校验（参数是否合理）→ 代码审查（语法/逻辑）→ 硬件检查（内存/GPU是否够）
- post 审查：结果质量评估（输出是否合理）→ 图表检查（图是否生成）→ 数值检查（细胞数/基因数是否异常）→ 错误检查（有无 warning/error）
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 · 209 lines · 29 tokens per session scan A e6df2d20e17e
doubletfinder-remove-doublets is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 2,452 once invoked, about $0.0001 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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