bulk-rnaseq-counts-to-de-deseq2

bulk-rnaseq-counts-to-de-deseq2 is a skill for Claude Code from GGbond-bo/MemOmics-Agent. It costs 12 tokens per session (9,078 once invoked), scanned A, a copy of Bulk RNAseq differential expression (DeSeq2), MIT.

A workflow for analysing bulk RNA sequencing count data with DESeq2, a method for finding genes whose activity differs between conditions. It also imposes staged checks, parameter comparisons, and review steps during the analysis.

In plain words
What is it for?
Use it to process bulk RNA-seq counts, compare experimental conditions, test analysis parameters, and review the resulting outputs.
Why use it?
It helps prevent an analysis from being run as one unchecked script or with unexplained parameter choices. Each stage is checked before the next one begins.

Skill for Claude Code

Written for Claude Code: when-to-use in frontmatter.

Good fit Use it to process bulk RNA-seq counts, compare experimental conditions, test analysis parameters, and review the resulting outputs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2
Install

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.

Any agent
npx skills add GGbond-bo/MemOmics-Agent --skill bulk-rnaseq-counts-to-de-deseq2
Clone the repo
git clone --depth 1 https://github.com/GGbond-bo/MemOmics-Agent

Made for: Claude Code.

Wrote 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.

agentmods badge for bulk-rnaseq-counts-to-de-deseq2

README.md
[![agentmods](https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2/github.svg)](https://agentmods.dev/skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2)
Your own site
<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2/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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/bulk-rnaseq-counts-to-de-deseq2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 9,078 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00012 $0.09078
Opus 5 $0.00006 $0.04539
Sonnet 5 $0.00002 $0.01816
Haiku 4.5 $0.00001 $0.00908

Measured 9d ago against content hash 86a4bcf1960d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

bulk-rnaseq-counts-to-de-deseq2 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/run.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

88% identical to Bulk RNAseq differential expression (DeSeq2) — 1,173 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.

hermes_home/skills/bioinformatics/bulk-rnaseq-counts-to-de-deseq2/SKILL.md · 664 lines

How it starts

The opening of the file, as written. The whole thing — 664 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。以下规则覆盖所有 Biomni 默认行为。

规则1: 拿到数据 → 必须调 search_knowledge

  • 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: 7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. check_env 检查环境
3. rail_review(pre) 前置审查
4. source/import 预写脚本(禁止 inline 代码)
5. terminal 执行(分步执行,禁止 && 连接多步骤)
6. debate_analysis 多方辩论(正方/反方切断上下文独立生成 + LLM裁决)
7. rail_review(post) 后置审查

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:正方(支持当前参数)vs 反方(质疑+替代方案)→ 裁判决断
  • 不确定的参数就辩论,不要自己拍脑袋

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

规则6: 结果存储结构

results/<模块>/<方法>/
  ├── scripts/     # 分析脚本
  ├── figures/     # PNG + SVG 图表
  ├── data/        # RDS/H5AD 中间数据
  └── results/     # CSV/TSV 结果表

Read the full file on GitHub · 664 lines

Changes

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.

  1. 9d ago First seen · 664 lines · 12 tokens per session scan A 86a4bcf1960d

Subscribe to this mod's changes

bulk-rnaseq-counts-to-de-deseq2 is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 12 tokens to every session and 9,078 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to Bulk RNAseq differential expression (DeSeq2), differing in 1,173 lines, and is treated as a copy.