claude-code-workshop
blakelash/claude-code-workshop
This skill provides a structured workflow for performing bulk RNA-seq differential expression analysis using pydeseq2, including validation, filtering, statistical testing, and diagnostic plotting.
3 5MITUpdated 2026-04-03
70
GoodOverview
Designed for bioinformaticians and computational biologists, this skill automates the execution of differential gene expression analysis using the Python-based pydeseq2 package. It handles count matrix and metadata validation, low-count pre-filtering, statistical model fitting, Benjamini-Hochberg correction, and generates standard publication-ready diagnostic plots such as MA, volcano, PCA, and dispersion plots.
Capabilities
- ▸Validates input count matrices and metadata formats.
- ▸Filters out genes with low total counts.
- ▸Fits statistical models and computes differential expression via pydeseq2.
- ▸Generates colorblind-safe diagnostic visualizations including MA and volcano plots.
Best for
Identifying differentially expressed genes between two experimental conditions from a raw count matrix., Generating standard RNA-seq diagnostic and quality control plots (PCA, volcano, MA, dispersion)., Automating bioinformatic pipeline steps inside AI coding environments.
Works with
Claude CodeCursor
Evoa Score breakdown= Σ (score × weight)
70Task usefulness20%77 → +15.4
実タスクにどれだけ役立つか（機能の豊富さ・用途の明確さ）。 — AIによるcapabilities/use-cases解析
Code quality15%90 → +13.5
実装・指示の品質。 — AIによるSKILL.md/README解析
Maintenance15%70 → +10.5
リポジトリがどれだけ活発に保守されているか。 — GitHub 最終push日時の新しさ
Documentation12%31 → +3.7
ドキュメントの充実度・分かりやすさ。 — README/独自要約の情報量
Security15%100 → +15.0
危険・不審な挙動が無いか。 — AIによるセキュリティレビュー
Originality10%75 → +7.5
ありふれたラッパーではない独自性。 — AIによる独自性判定
Popularity8%29 → +2.3
コミュニティの採用度。 — GitHub Stars/Forks（対数スケール）
Compatibility5%50 → +2.5
対応AIエージェントの広さ。 — AIによる対応エージェント判定
Weighted total70.4 / 100
ライセンス不明/制限あり（Red）のSkillは総合スコアに0.85倍の補正を適用します。 ランキングはこのScoreのみで決まり、広告で変わりません。 算出方法の詳細 →
Security considerations
The skill executes standard local Python code processing user-supplied CSV files. No external network requests or sensitive API keys are required.
Categories
Summary and analysis are original content generated by AI Skills Rank. The skill's source text is not reproduced here — view it on the linked repository.