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ruview-model-training
Train RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-…
orchestration
Coordinate supervised Orca workers: threaded messages, blocking ask/reply, task dispatch, worker_done/escalation waits, task DAGs, decision gates, coordinator loops, and decomposing work across agents. Use `orca-cli` for full ownership handoffs — "hand off", "handoff", "handover", "give this to another agent", "another worktree" — unless asked to supervise,…
scikit-learn
Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniq…
torchdrug
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
umap-learn
Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
anti-sleep
Keep a Mac awake with caffeinate during long builds, downloads, or supervised automation runs.
yann-lecun-tecnico
Sub-skill técnica de Yann LeCun. Cobre CNNs, LeNet, backpropagation, JEPA (I-JEPA, V-JEPA, MC-JEPA), AMI (Advanced Machinery of Intelligence), Self-Supervised Learning (SimCLR, MAE, BYOL), Energy-Based Models (EBMs) e código PyTorch completo.
constitutional-ai
Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
scvi-tools
Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster.…
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned mo…
tooluniverse-scvi-tools
Deep learning for single-cell omics using scvi-tools — probabilistic (VAE-based) batch correction and integration with scVI, semi-supervised integration and label transfer with scANVI, CITE-seq RNA+protein joint modeling with totalVI, scATAC-seq analysis with PeakVI, multiome RNA+ATAC joint modeling with MultiVI, spatial transcriptomics cell-type deconvolut…
tooluniverse-scvi-tools
Deep learning for single-cell omics using scvi-tools — probabilistic (VAE-based) batch correction and integration with scVI, semi-supervised integration and label transfer with scANVI, CITE-seq RNA+protein joint modeling with totalVI, scATAC-seq analysis with PeakVI, multiome RNA+ATAC joint modeling with MultiVI, spatial transcriptomics cell-type deconvolut…
bio-crispr-screens-copy-number-correction
Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the gene-independent DNA-damage / G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-eff…
bio-flow-cytometry-clustering-phenotyping
Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependenc…
bio-immunoinformatics-tcr-epitope-binding
Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does n…
bio-metabolomics-statistical-analysis
Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and depe…
bio-multi-omics-mixomics-analysis
Builds supervised and unsupervised multivariate integration across bulk omics blocks with mixOmics - sPLS for sparse pairwise correlation, DIABLO (block.splsda) for a multi-block discriminant signature, rCCA for regularized canonical correlation, and MINT for multi-study integration. Covers why these projection methods maximize covariance or correlation and…
bio-multi-omics-mofa-integration
Discovers shared and view-specific latent factors across bulk multi-omics blocks (RNA-seq, proteomics, methylation) on a common sample axis with MOFA2's unsupervised Bayesian group factor model, then attributes per-view variance explained and interprets signed factor weights. Covers why a factor is an unsupervised axis of variance and not a pathway, why a f…
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Sources and import coverage