debug-conda
73Skill OpenCode
Diagnose and repair conda environment errors.
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Diagnose and repair conda environment errors.
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Diagnose and repair missing input or reference files.
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Diagnose and repair missing Python package.
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Diagnose and repair reference genome or annotation mismatch.
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Diagnose and repair missing CLI tool.
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Define scRNA-seq input requirements and generic analysis conventions.
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Define tumor research modes and route tumor scRNA-seq pipelines.
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Python conda environment for scRNA-seq tools.
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R/Bioconductor environment for auxiliary oncology plots.
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Bulk ATAC-seq processing: assay QC gates, MACS3 fragment-aware peak calling, consensus peak matrices, differential accessibility, and motif/footprint follow-up.
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Run cNMF co-expression module analysis.
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Run inferCNVpy malignant cell identification.
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Run Palantir state trajectory analysis.
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Run Scanpy QC, preprocessing, clustering, and annotation steps.
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Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible cohort-ready output tables.
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Spatially informed clustering of spatial transcriptomics data into spatial domains using GraphST (graph self-supervised contrastive learning + GNN).
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Spatial domain identification with GreS, a semantic-guided spatial representation learning framework that injects gene-level functional priors into GCN-based spot embeddings and clusters them with k-means.
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Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…
Skill OpenCode
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
Skill OpenCode
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file…
Skill OpenCode
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE…
Skill OpenCode
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological…
Skill OpenCode
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: