菜单
Single-cell RNA-seq analysis (mirrors Claude Science's single-cell skill). scanpy/anndata pipeline QC->normalize->HVG->PCA->neighbors->Leiden->UMAP->markers->annotation, publication figures. Use for scRNA-seq, count matrices, cell clustering/annotation. Needs the sci-bio env.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Cheminformatics (mirrors Claude Science's cheminformatics skill). RDKit for descriptors/fingerprints/similarity and molecule drawing, ChEMBL for bioactivity, SAR/screening. Use for small molecules, SMILES, molecular descriptors, ChEMBL, medicinal chemistry. Needs the sci-bio env (rdkit).
CRISPR screen design & analysis (mirrors Claude Science's CRISPR screen skill). Designs guide libraries, runs enrichment/depletion analysis on read counts, and traces every hit back to its sgRNA support. Use for sgRNA libraries, screen counts, enrichment analysis.
Reproducible data analysis. Uses pandas/numpy/scipy/statsmodels for cleaning, statistics, and modeling; scripts and random seeds all land in the run, and every reported number is recomputable. Use to analyze tabular/matrix data, run statistical tests, or model.
Publication-grade figures. Uses matplotlib to generate figures from a script (never hand-drawn, never pasted from an unknown source); each figure has a rerunnable script. Layer the global CCF-Figure skill for figure conventions / top-venue layout. Use to draw paper figures and iterate to publication quality.
Provenance-backed literature review. Searches PubMed / Europe PMC / arXiv (connectors/literature.py), dedupes, synthesizes by theme, and attaches a DOI/PMID to every conclusion. Use when you need a literature review, evidence for a claim, or a related-work section.
Manuscript writing and iteration to publication quality. Organizes IMRaD structure, inserts run-generated figures/tables, manages references, and produces docx/pdf/md. Layer the global docx / doc-coauthoring / pdf skills. Use to write a paper draft, methods, results, and iterate.
| name | single-cell-rnaseq |
| description | Single-cell RNA-seq analysis (mirrors Claude Science's single-cell skill). scanpy/anndata pipeline QC->normalize->HVG->PCA->neighbors->Leiden->UMAP->markers->annotation, publication figures. Use for scRNA-seq, count matrices, cell clustering/annotation. Needs the sci-bio env. |
Depends on
scanpy,anndata(see thesci-bioenv in environment-bio.yml). Gene annotation viaconnectors/ensembl.py/uniprot.py.
pct_counts_mt, n_genes, doublets; record filter thresholds and pre/post cell counts.normalize_total + log1p + highly_variable_genes + scale.pca -> neighbors -> leiden -> umap.rank_genes_groups for markers -> annotate cell types (state the basis).