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 analyze_radiolabeled_antibody_biodistributiongit 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/analyze_radiolabeled_antibody_biodistribution)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/analyze_radiolabeled_antibody_biodistribution"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/analyze_radiolabeled_antibody_biodistribution/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/analyze_radiolabeled_antibody_biodistribution"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/analyze_radiolabeled_antibody_biodistribution.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 | $0.00028 | $0.01197 |
| Opus 5 | $0.00014 | $0.00598 |
| Sonnet 5 | $0.00006 | $0.00239 |
| Haiku 4.5 | $0.00003 | $0.00120 |
Grade A, and why
analyze_radiolabeled_antibody_biodistribution 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.
This is a copy
89% identical to analyze_fda_safety_signals — 19 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Radiolabeled Antibody Biodistribution
Analyze biodistribution and pharmacokinetic profile of radiolabeled antibodies.
When to Use
When you need analyze radiolabeled antibody biodistribution analysis
Parameters
| Parameter | Default | Notes |
|---|---|---|
time_points |
[Required] Time points (hours) at which measurements were taken (List[float] or numpy.ndarray) | |
tissue_data |
[Required] Dictionary where keys are tissue names and values are lists/arrays of %IA/g measurements corresponding to time_points. Must include 'tumor' as one of the keys (dict) |
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: drug_discovery
- 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
- 分科知识库：生物学编辑用 biology_kb / 统计学编辑用 statistics_kb / 生信编辑用 bioinfo_kb / 历史经验编辑用 history_errors
- 辩论结果自动归档到 results/.../log/debate_*.json
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。
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 · 104 lines · 28 tokens per session scan A 62d3d7b4e7f1
analyze_radiolabeled_antibody_biodistribution is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 1,197 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to analyze_fda_safety_signals, differing in 19 lines, and is treated as a copy.
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