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O SkillsMP coletou 810 skills de mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-. Abra uma skill para revisar a origem e os detalhes.
mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-Mostrando 40 de 810 skills coletadas.
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 do texto original: inglês
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 do texto original: inglês
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 do texto original: inglês
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 do texto original: inglês
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 do texto original: inglês
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 do texto original: inglês
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 do texto original: inglês
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 do texto original: inglês
Deploy AgentScope + AgentScope Runtime for secure sandboxed multi-agent services inside BioKernel.
Idioma do texto original: inglês
Run OpenHands headless CLI/SDK missions from BioKernel swarms for autonomous software work.
Idioma do texto original: inglês
Parse scholarly articles (PDF, DOI, URL) to extract metadata, GEO accessions, and acquisition links using OpenAlex + GROBID pipelines.
Idioma do texto original: inglês
Cell type annotation for spatial transcriptomics data using marker-based scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.
Idioma do texto original: inglês
Copy number variation inference from spatial transcriptomics expression data.
Idioma do texto original: inglês
Cell-cell communication analysis via ligand-receptor interaction scoring using LIANA, CellPhoneDB, FastCCC, or CellChat.
Idioma do texto original: inglês
Experimental condition comparison using pseudobulk differential expression with proper multi-sample statistics.
Idioma do texto original: inglês
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 do texto original: inglês
Cell type deconvolution for spatial transcriptomics — estimates per-spot cell type proportions using FlashDeconv, Cell2Location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, or CARD.
Idioma do texto original: inglês
Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, or BANKSY.
Idioma do texto original: inglês
Pathway and gene set enrichment analysis for spatial transcriptomics data.
Idioma do texto original: inglês
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 do texto original: inglês
Multi-sample integration and batch correction for spatial transcriptomics data.
Idioma do texto original: inglês
Load spatial transcriptomics data (Visium, Xenium, MERFISH, Slide-seq, generic h5ad), perform QC filtering, normalization, HVG selection, PCA, UMAP, and Leiden clustering.
Idioma do texto original: inglês
Spatial registration and multi-slice alignment for spatial transcriptomics data.
Idioma do texto original: inglês
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 do texto original: inglês
Trajectory inference and pseudotime analysis for spatial transcriptomics data.
Idioma do texto original: inglês
RNA velocity and cellular dynamics analysis for spatial transcriptomics data.
Idioma do texto original: inglês
Bulk RNA-seq count matrix QC — library size, gene detection rates, and sample correlation.
Idioma do texto original: inglês
WGCNA-style weighted gene co-expression network analysis — module detection, soft thresholding, hub genes.
Idioma do texto original: inglês
Bulk RNA-seq differential expression analysis using PyDESeq2 with optional edgeR/limma-voom via rpy2.
Idioma do texto original: inglês
Bulk RNA-seq cell type deconvolution using NNLS (built-in), with optional CIBERSORTx and MuSiC bridges.
Idioma do texto original: inglês
Pathway enrichment analysis for bulk RNA-seq — ORA and GSEA via GSEApy, with built-in hypergeometric fallback.
Idioma do texto original: inglês
Alternative splicing analysis — PSI quantification, differential splicing event detection from rMATS/SUPPA2 output.
Idioma do texto original: inglês
Batch integration for multi-sample scRNA-seq using Harmony, scVI, Seurat CCA/RPCA, BBKNN, and fastMNN. Remove technical variation while preserving biological differences.
Idioma do texto original: inglês
Automated cell type annotation using marker genes, CellTypist, SingleR, or scmap. Supports custom references and marker gene lists.
Idioma do texto original: inglês
Cell-cell communication analysis via ligand-receptor interaction scoring using CellChat (R), NicheNet (R), LIANA (Python), or built-in L-R database.
Idioma do texto original: inglês
Differential expression analysis for single-cell data — marker gene discovery using Wilcoxon, t-test, MAST, or DESeq2 pseudo-bulk analysis.
Idioma do texto original: inglês
Doublet detection and removal using Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering.
Idioma do texto original: inglês
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 do texto original: inglês
Multi-omics integration for single-cell data (CITE-seq, 10X Multiome, SHARE-seq). Weighted Nearest Neighbor (WNN) analysis, MOFA+, and muon/MuData workflows.
Idioma do texto original: inglês
Single-cell RNA-seq QC, normalization, HVG selection, PCA, UMAP, and Leiden clustering. Supports both Scanpy (Python) and Seurat (R) workflows.
Idioma do texto original: inglês