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SkillsMP ha recopilado 810 skills de mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-. Abre una skill para revisar su origen y sus detalles.
mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-Mostrando 40 de 810 skills recopiladas.
Design and operate browser and desktop agents that rely on screenshots, mouse and keyboard control, or hybrid bash/editor/computer loops. Use when deciding between DOM automation, browser agents, and full computer-use workflows.
Idioma del texto original: inglés
Implement and operate Azure AI Foundry and Microsoft Foundry workloads with explicit identity, deployment, model versioning, safety, and agent-service controls. Use when deploying model endpoints, migrating model versions, or setting up production guardrails…
Idioma del texto original: inglés
Integrate and operate Cohere APIs with current model, rerank, embedding, transcribe, and SDK guidance. Use when selecting Cohere models, building search or agent workflows, or planning migration across Cohere platform updates.
Idioma del texto original: inglés
Build, evaluate, and deploy agents with Google's Agent Development Kit (ADK). Use when you want code-first multi-agent systems, workflow agents, MCP tools, or Google-supported agent deployment paths.
Idioma del texto original: inglés
Build typed, provider-agnostic agents with PydanticAI. Use when structured I/O, dependency injection, MCP support, and OpenTelemetry-friendly observability matter more than framework hype.
Idioma del texto original: inglés
Integrate and operate DeepSeek APIs with current docs and compatibility guidance. Use when implementing DeepSeek chat, reasoning, tool calling, or FIM workflows through its OpenAI-compatible API.
Idioma del texto original: inglés
Integrate and operate Mistral APIs with current model catalog, SDKs, and agent features. Use when implementing Mistral chat, agents, conversations, files, or coding workflows.
Idioma del texto original: inglés
Integrate and operate xAI Grok APIs with current documentation and SDK guidance. Use when implementing Grok tool use, Responses-style workflows, files or collections search, or migration from other provider SDKs.
Idioma del texto original: inglés
Deploy AgentScope + AgentScope Runtime for secure sandboxed multi-agent services inside BioKernel.
Idioma del texto original: inglés
Run OpenHands headless CLI/SDK missions from BioKernel swarms for autonomous software work.
Idioma del texto original: inglés
Parse scholarly articles (PDF, DOI, URL) to extract metadata, GEO accessions, and acquisition links using OpenAlex + GROBID pipelines.
Idioma del texto original: inglés
Cell type annotation for spatial transcriptomics data using marker-based scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.
Idioma del texto original: inglés
Copy number variation inference from spatial transcriptomics expression data.
Idioma del texto original: inglés
Cell-cell communication analysis via ligand-receptor interaction scoring using LIANA, CellPhoneDB, FastCCC, or CellChat.
Idioma del texto original: inglés
Experimental condition comparison using pseudobulk differential expression with proper multi-sample statistics.
Idioma del texto original: inglés
Differential expression analysis — find marker genes for clusters or compare two groups. Supports Wilcoxon rank-sum, t-test, and PyDESeq2 methods with publication-ready figures and CSV tables.
Idioma del texto original: inglés
Cell type deconvolution for spatial transcriptomics — estimates per-spot cell type proportions using FlashDeconv, Cell2Location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, or CARD.
Idioma del texto original: inglés
Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, or BANKSY.
Idioma del texto original: inglés
Pathway and gene set enrichment analysis for spatial transcriptomics data.
Idioma del texto original: inglés
Find genes with spatially variable expression patterns using Moran's I, SpatialDE, SPARK-X, or FlashS. Identifies genes whose expression is non-randomly distributed across tissue coordinates.
Idioma del texto original: inglés
Multi-sample integration and batch correction for spatial transcriptomics data.
Idioma del texto original: inglés
Load spatial transcriptomics data (Visium, Xenium, MERFISH, Slide-seq, generic h5ad), perform QC filtering, normalization, HVG selection, PCA, UMAP, and Leiden clustering.
Idioma del texto original: inglés
Spatial registration and multi-slice alignment for spatial transcriptomics data.
Idioma del texto original: inglés
Comprehensive spatial statistics toolkit — cluster-level (neighborhood enrichment, Ripley, co-occurrence), gene-level (Moran's I, Geary's C, local Moran, Getis-Ord), and network-level analysis.
Idioma del texto original: inglés
Trajectory inference and pseudotime analysis for spatial transcriptomics data.
Idioma del texto original: inglés
RNA velocity and cellular dynamics analysis for spatial transcriptomics data.
Idioma del texto original: inglés
Bulk RNA-seq count matrix QC — library size, gene detection rates, and sample correlation.
Idioma del texto original: inglés
WGCNA-style weighted gene co-expression network analysis — module detection, soft thresholding, hub genes.
Idioma del texto original: inglés
Bulk RNA-seq differential expression analysis using PyDESeq2 with optional edgeR/limma-voom via rpy2.
Idioma del texto original: inglés
Bulk RNA-seq cell type deconvolution using NNLS (built-in), with optional CIBERSORTx and MuSiC bridges.
Idioma del texto original: inglés
Pathway enrichment analysis for bulk RNA-seq — ORA and GSEA via GSEApy, with built-in hypergeometric fallback.
Idioma del texto original: inglés
Alternative splicing analysis — PSI quantification, differential splicing event detection from rMATS/SUPPA2 output.
Idioma del texto original: inglés
Batch integration for multi-sample scRNA-seq using Harmony, scVI, Seurat CCA/RPCA, BBKNN, and fastMNN. Remove technical variation while preserving biological differences.
Idioma del texto original: inglés
Automated cell type annotation using marker genes, CellTypist, SingleR, or scmap. Supports custom references and marker gene lists.
Idioma del texto original: inglés
Cell-cell communication analysis via ligand-receptor interaction scoring using CellChat (R), NicheNet (R), LIANA (Python), or built-in L-R database.
Idioma del texto original: inglés
Differential expression analysis for single-cell data — marker gene discovery using Wilcoxon, t-test, MAST, or DESeq2 pseudo-bulk analysis.
Idioma del texto original: inglés
Doublet detection and removal using Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering.
Idioma del texto original: inglés
Gene regulatory network inference using pySCENIC three-step pipeline (GRNBoost2 → cisTarget → AUCell), with correlation-based fallback. Identifies transcription factor regulons and scores their activity per cell.
Idioma del texto original: inglés
Multi-omics integration for single-cell data (CITE-seq, 10X Multiome, SHARE-seq). Weighted Nearest Neighbor (WNN) analysis, MOFA+, and muon/MuData workflows.
Idioma del texto original: inglés
Single-cell RNA-seq QC, normalization, HVG selection, PCA, UMAP, and Leiden clustering. Supports both Scanpy (Python) and Seurat (R) workflows.
Idioma del texto original: inglés