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 estimate_alpha_particle_radiotherapy_dosimetrygit 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/estimate_alpha_particle_radiotherapy_dosimetry)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/estimate_alpha_particle_radiotherapy_dosimetry"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/estimate_alpha_particle_radiotherapy_dosimetry/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/estimate_alpha_particle_radiotherapy_dosimetry"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/estimate_alpha_particle_radiotherapy_dosimetry.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 | $0.00039 | $0.01288 |
| Opus 5 | $0.00019 | $0.00644 |
| Sonnet 5 | $0.00008 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
estimate_alpha_particle_radiotherapy_dosimetry 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Estimate Alpha Particle Radiotherapy Dosimetry
Estimate radiation absorbed doses to tumor and normal organs for alpha-particle radiotherapeutics using the Medical Internal Radiation Dose (MIRD) schema.
When to Use
When you need estimate alpha particle radiotherapy dosimetry analysis
Parameters
| Parameter | Default | Notes |
|---|---|---|
biodistribution_data |
[Required] Dictionary containing organ/tissue names as keys and a list of time-activity measurements as values. Each measurement should be a tuple of (time_hours, percent_injected_activity). Must include entries for all relevant organs including 'tumor'. (dict) | |
radiation_parameters |
[Required] Dictionary containing radiation parameters for the alpha-emitting radionuclide including 'radionuclide', 'half_life_hours', 'energy_per_decay_MeV', 'radiation_weighting_factor', and 'S_factors'. (dict) | |
output_file |
[Optional] Filename to save the dosimetry results (default: dosimetry_results.csv) |
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
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 · 105 lines · 39 tokens per session scan A db925a0606e0
estimate_alpha_particle_radiotherapy_dosimetry is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 1,288 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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