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SkillsMP 已收集 mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- 中的 810 个 Skill。打开任一 Skill 可查看来源和详情。
mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-已展示 40 / 810 个已收集 Skill。
Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals. Skip when QC thresholds are still undecided (use sc-qc) or for batch correction across samples (use sc-batch-integration).
原文语言：英语
Load when ordering cells along a developmental trajectory in a normalised scRNA AnnData via DPT, Palantir, VIA, CellRank, Slingshot (R), or Monocle3 (R). Skip when ranking marker genes per cluster (use sc-markers) or for RNA velocity vector fields (use…
原文语言：英语
Load when computing per-cell QC metrics (n_genes, total counts, mt%, ribo%) on a single-cell AnnData before filtering. Skip when reads are still raw FASTQ (use sc-fastq-qc) or you want to filter cells now (use sc-filter).
原文语言：英语
Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run. Skip when data already came from sc-count (already canonical), or for bulk RNA-seq (use bulkrna-qc) or spatial…
原文语言：英语
Load when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity. Skip when AnnData already has spliced+unspliced layers (go straight to sc-velocity) or for any non-velocity…
原文语言：英语
Load when computing RNA velocity vectors and latent time on a scRNA AnnData with spliced / unspliced layers via scVelo (stochastic / dynamical / steady-state). Skip when input lacks spliced+unspliced layers (run sc-velocity-prep first) or for trajectory…
原文语言：英语
Multi-method consensus over spatial-domains. Fans out 5 methods in parallel, computes a SACCELERATOR-style base-clustering ranking, runs typed consensus (kmode / weighted / LCA), and emits a verified consensus report with the mandatory A-path banner per ADR…
原文语言：英语
LLM-grounded biological interpretation of a verified typed consensus run. Reads the typed run dir + the original adata, runs inline per-cluster DE, looks up markers in a bundled tissue-keyed marker DB, and asks the chair LLM to (γ) name each cluster's likely…
原文语言：英语
Load when assigning per-spot cell-type labels on a spatial AnnData via marker-gene scoring or scRNA-reference mapping (Tangram / scANVI / CellAssign). Skip when computing spot-level cell-type proportions for multi-cell-per-spot platforms (use spatial-deconv)…
原文语言：英语
Load when inferring copy-number variation per spot on a preprocessed spatial AnnData with chromosome-annotated genes via infercnvpy (default — log-ratio sliding-window) or Numbat (R, allele-aware clone deconvolution). Skip when `var["chromosome"]` /…
原文语言：英语
Load when computing ligand-receptor cell-cell communication on a preprocessed spatial AnnData with `obs[cell_type_key]` (default `leiden`) via LIANA (default), CellPhoneDB, FastCCC, or CellChat (R). Skip when running scRNA-only L-R inference (use…
原文语言：英语
Load when comparing two or more experimental conditions (treatment vs control) on a multi-sample preprocessed spatial AnnData via PyDESeq2 pseudobulk or Wilcoxon DE — needs `obs[condition_key]`, `obs[sample_key]`, and cluster labels. Skip when running…
原文语言：英语
Load when ranking spatial cluster markers or comparing two spatial groups in spatial transcriptomics. Skip if the data is single-cell (use sc-de) or bulk (use bulkrna-de), or for spatially variable expression discovery (use spatial-genes).
原文语言：英语
Load when deconvolving spot-level cell-type proportions on a Visium-style spatial AnnData using a labelled scRNA reference (FlashDeconv / Cell2location / RCTD / DestVI / Tangram / others). Skip when each spot is a single cell already (Xenium / MERFISH — use…
原文语言：英语
Load when detecting tissue domains / niches on a preprocessed spatial AnnData via Leiden / Louvain (spatial-weighted) or graph-neural backends (SpaGCN / STAGATE / GraphST / BANKSY / CellCharter). Skip when ranking spatially variable genes (use spatial-genes)…
原文语言：英语
Load when running pathway / gene-set enrichment per cluster on a preprocessed spatial AnnData via Enrichr (over-representation), GSEA (preranked), or ssGSEA (per-cell scores). Skip when ranking spatially variable genes (use `spatial-genes`) or when comparing…
原文语言：英语
Load when ranking spatially variable genes (SVGs) on a preprocessed spatial AnnData via Moran's I, SpatialDE, SPARK-X, or FlashS. Skip when detecting tissue domains (use spatial-domains) or for differential expression between groups (use spatial-de).
原文语言：英语
Load when removing batch effects across multiple spatial samples on a multi-batch spatial AnnData via Harmony, BBKNN, or Scanorama before downstream analysis. Skip when aligning physical slice coordinates (use spatial-register) or for single-batch data (no…
原文语言：英语
Load when extracting a niche / microenvironment subset around a center cell-type by spatial radius from a labelled spatial AnnData, producing a smaller AnnData of centers + their within-radius neighbours. Skip when running global tissue-domain detection (use…
原文语言：英语
Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData. Skip when raw FASTQs need converting first (use spatial-raw-processing)…
原文语言：英语
Load when converting spatial transcriptomics raw FASTQ pairs through ST-Pipeline into a `raw_counts.h5ad` ready for spatial-preprocess. Skip when input is already a count-matrix AnnData (go straight to spatial-preprocess) or for non-spatial bulk / scRNA FASTQ…
原文语言：英语
Load when aligning multiple spatial slices into a common coordinate frame on a multi-slice spatial AnnData via PASTE optimal transport or STalign image-aware registration. Skip when data is single-slice (no registration needed) or for cross-sample integration…
原文语言：英语
Load when running spatial autocorrelation / hotspot / co-occurrence / neighbourhood-enrichment / Ripley K stats on a clustered spatial AnnData via squidpy. Skip when ranking spatially variable genes (use spatial-genes) or for tissue domain detection (use…
原文语言：英语
Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint branch probabilities). Skip when the data has…
原文语言：英语
Load when estimating RNA velocity on a spatial AnnData with `layers["spliced"]` + `layers["unspliced"]` via scVelo (stochastic / deterministic / dynamical) or veloVI (deep generative). Skip when input lacks the spliced/unspliced layers (must be quantified…
原文语言：英语
Load when copying this directory to bootstrap a new OmicsClaw v2 skill (rename, fill in, then `git add`). Skip when an existing skill already covers the request.
原文语言：英语
Build and run production workloads on Amazon Bedrock with current model availability, Converse API, agents, guardrails, AgentCore, and IAM controls. Use when implementing Bedrock inference pipelines, managed agents, or provider-agnostic model routing on AWS.
原文语言：英语
Design evaluation, tracing, monitoring, scope-control, and rollback discipline for agent systems. Use when an agent workflow is becoming important enough that you need evidence, not vibes, to decide whether it is good.
原文语言：英语
Build and operate OpenAI-first coding and agent workflows using Codex app/cloud, the Responses API, current GPT and Codex models, Agents SDK, hosted tools, tool search, MCP/connectors, skills, and approval-aware tool execution. Use when you need long-horizon…
原文语言：英语
Design, evaluate, and operate agentic systems for biomedical and scientific discovery. Use when building or selecting agents for hypothesis generation, experiment planning, autonomous notebook analysis, lab-in-the-loop validation, pathology concept discovery,…
原文语言：英语
Implement and operate Model Context Protocol systems safely. Use when designing MCP clients or servers, selecting transports, configuring auth, onboarding remote servers, or enforcing approval and egress controls.
原文语言：英语
Batch effect correction for multi-cohort bulk RNA-seq data using ComBat, with PCA-based visualization before and after correction.
原文语言：英语
WGCNA-style weighted gene co-expression network analysis — module detection, soft thresholding, hub genes.
原文语言：英语
Differential expression analysis via PyDESeq2 with Welch's t-test fallback — volcano plots, MA plots, p-value diagnostics.
原文语言：英语
Bulk RNA-seq cell type deconvolution using NNLS (built-in), with optional CIBERSORTx and MuSiC bridges.
原文语言：英语
Pathway enrichment analysis for bulk RNA-seq — ORA and GSEA via GSEApy, with built-in hypergeometric fallback.
原文语言：英语
Gene identifier conversion between Ensembl, Entrez, HGNC symbols, and UniProt for bulk RNA-seq count matrices.
原文语言：英语
Protein-protein interaction network analysis from DEG lists — STRING API query, graph construction, hub gene identification.
原文语言：英语
Bulk RNA-seq count matrix quality control — library sizes, gene detection, sample correlation, outlier detection, CPM normalization.
原文语言：英语
RNA-seq read alignment and quantification statistics — STAR/HISAT2/Salmon log parsing, mapping rate, unique/multi-mapped reads, library strandedness, gene body coverage.
原文语言：英语