Run metadata-aware predictions and fine-tune AlphaGenome-PyTorch on custom genomic tracks.
Plugin alphagenome from marketplace alphagenome in genomicsxai/alphagenome-pytorch. Commands read from the repo's manifest — 1 month ago.
alphagenome-finetuningFine-tune or transfer-learn AlphaGenome-PyTorch on custom genomic data — pick a mode (linear probe, LoRA, Locon, full), train on BigWig tracks with `agt finetune`, use adapters, delta checkpoints, multi-GPU/sequence parallelism, or the Python transfer API. Use when ADAPTING/TRAINING the model on new
alphagenome-predictionsRun AlphaGenome-PyTorch to get genomic track predictions — via the `agt predict` CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant effect scoring (`agt score`), or the Python API. Covers picking a specific assay, cell type, or resoluti
MetaHarness integration — surfaces score/genome/mint/mcp-scan/threat-model via skills; pairs with @metaharness/router (ADR-148/149) for cost-optimal model routing; honors ADR-150 optional-augmentation constraint
13 skills · 1 command · 1 agent · hooks
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Multi-GPU and multi-node training including DeepSpeed, PyTorch FSDP, Accelerate, Megatron-Core, PyTorch Lightning, and Ray Train. Use when training large models across GPUs.
98 skills
Query the KEGG bioinformatics database — pathways, genes, compounds, drugs, enzymes, diseases, reactions, and more via 34 read-only MCP tools. No API key required.
1 skill · 3 commands · 1 agent · 1 MCP server
Build and configure neural network architectures
1 skill · 1 command
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