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 deg-analysisgit 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/deg-analysis)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/deg-analysis"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/deg-analysis/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/deg-analysis"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/deg-analysis.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 | $0.00057 | $0.03912 |
| Opus 5 | $0.00028 | $0.01956 |
| Sonnet 5 | $0.00011 | $0.00782 |
| Haiku 4.5 | $0.00006 | $0.00391 |
Grade A, and why
deg-analysis 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⛔ MemOmics 强制规则（不可违反，优先级最高）
本 skill 已集成到 MemOmics-Agent 自进化生信分析平台。使用本 skill 前，必须先通过 skill_view 加载本文件。以下规则覆盖所有默认行为。
规则1: 写代码前 → 必须先 search_knowledge + skill_view
- 每个分析步骤写代码前，必须先调
search_knowledge(species=..., tissue=..., direction=..., query="<步骤名> 参数") - 知识库有匹配 → 用知识库的参数和模板
- 知识库无匹配 → 用 web 搜索文献，提取方法和参数，存入知识库
- 绝对不能跳过直接写代码
规则2: 8步循环（每步必须走完整循环）
1. search_knowledge 查本步骤的方法和参数
2. skill_view 加载本 SKILL.md（获取脚本模板+审查规则+参数范围）
3. check_env 检查环境（缺包自动安装）
4. rail_review(pre) 前置审查（参数合理吗？包齐了吗？数据准备好了吗？）
5. 写这一步的代码（基于 skill 模板，只写这一步，不写后续步骤）
6. terminal 执行（分步执行，禁止 && 连接多步骤）
7. debate_analysis 多方辩论（正方/反方切断上下文独立生成 + LLM裁决）
8. rail_review(post) 后置审查（图有没有？结果合理吗？跟知识库对应吗？）
规则3: 代码分段执行 — 写一步跑一步
- ❌ 禁止一次性写完全部代码用 && 连接执行
- ✅ 必须分步：写一步 → 执行 → 检查结果 → 辩论 → 下一步
规则4: 关键参数多参数尝试 + 辩论
- 涉及数值参数时（如 resolution, n_pcs, min_features, FDR threshold 等），至少尝试 2-3 个值
- 每次参数变更后调
debate_analysis辩论"这个参数合理吗？结果有没有变好？" - 辩论格式（多角色对抗 v3）：
- 正方 3 位专业编辑（各自独立，互相不知道）：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，互相不知道，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
- 裁判编辑：看到所有 7 方论点，给出裁决 + 置信度（高/中/低）
- 上下文隔离：每个编辑独立 HTTP API 调用，messages 只有自己的 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
- 不确定的参数就辩论，不要自己拍脑袋
- 辩论最多 3 轮：3 轮后选最优参数结果
规则5: 执行后审查
规则N: 运行记录只是参考，不能跳过审查
-
skill_evolution(action="query_logs") 返回的历史运行日志仅供参数参考
-
即使有 quality_score=9.0 的历史日志，仍必须执行 rail_review(pre)、debate_analysis、rail_review(post)
-
禁止因"之前跑过"而跳过任何审查步骤
-
禁止直接用历史日志里的脚本运行而不经本次审查
-
运行日志是"参考"不是"免审凭证"
- 图片检查：
- 图有没有生成？没生成 → 强制重新执行
- 图片是否空白（全白/全黑/全单一色）？空白 → 强制重新出图
- 图片是否有 NA/缺失值（>10% 像素是 NA）？有 NA → 强制重新出图
- 图片大小是否过小（<5KB）？过小 → 强制重新出图
- 图片数量是否足够？（每步至少 1 张图，关键步骤至少 2-3 张）
- 代码质量检查：
- 代码行数是否合理？（过短可能偷懒，过长可能未分段）
- 代码是否有注释？
- 代码是否分段执行（禁止 && 连接多步骤）？
- 结果合理性：
- 数值范围是否合理？
- 跟知识库对应吗？
- 参数和结论辩论：
- 有参数的选择 → 必须调 debate_analysis 辩论
- 有结论输出 → 必须调 debate_analysis 辩论
- 不通过 → 修复重跑
- 通过 → 必须调 skill_evolution(action="record_run") 记录成功经验（skill_name/script_name/species/tissue/direction/params_used/result_summary/quality_score/notes） → 创建目录存储(figures/results/scripts/data) → 下一步
- 不通过 → 修复后重跑 → 成功后调 skill_evolution(action="record_run")；如果是脚本报错 → 调 skill_evolution(action="record_error") 记录根因+修复方案
- 图片检查：
What ships with it
4 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 · 259 lines · 57 tokens per session scan A 4e67eda36a47
deg-analysis is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 3,912 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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