🧬 nfcore-rnaseq-wrapper
Wrapper skill for running nf-core/rnaseq bulk RNA-seq preprocessing from FASTQ or BAM inputs with strict preflight, reproducibility outputs, and downstream handoff to ClawBio bulk RNA-seq DE skills.
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Wrapper skill for running nf-core/rnaseq bulk RNA-seq preprocessing from FASTQ or BAM inputs with strict preflight, reproducibility outputs, and downstream handoff to ClawBio bulk RNA-seq DE skills.
Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-...
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Programmatic Firecrawl usage, self-hosted operations, academic paper routing, recursive deep research, and raw corpus persistence.
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
Implement a PyTorch-based MoE-Mamba model featuring an input-dependent selection mechanism and Mixture of Experts (MoE) layer for text generation tasks, including data loading, training, and evaluation workflows.
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