Agent Skills Hub

wt

Manage isolated LlamaFarm development environments using git worktrees for parallel agent sessions and service testing.

pytorch-lightning

PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.

paper-reproduce

Systematic methodology for reproducing published academic papers using provided data, including sample selection, statistical verification, and automated reporting.

debug-cuda-crash

Tutorial for identifying and resolving CUDA runtime crashes using FlashInfer's API logging framework.

scientific-critical-thinking

Evaluate scientific claims and research methodology for rigor, bias, and validity. Use evidence-based frameworks like GRADE and Cochrane to analyze experiments, protocols, and study conclusions.

feature-prioritization

A structured decision-making tool that applies RICE, MoSCoW, Kano, and value-effort frameworks to prioritize software features, roadmap items, and build-vs-defer decisions with data-driven objectivity.

pymc

Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.

seaborn

Statistical visualization library for Python. Create publication-quality graphics like box plots, heatmaps, and violin plots with pandas integration and automatic statistical estimation.

flow-nexus-neural

Train and manage neural networks in distributed E2B sandboxes using the Flow Nexus platform, supporting custom architectures like Transformers, LSTMs, and GANs.

stable-baselines3

Production-ready reinforcement learning using Stable Baselines3. Train agents, design custom environments, implement training callbacks, and optimize workflows with a scikit-learn-style API.

statistical-analysis

Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting for academic and experimental research.

ml-pipeline-workflow

Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.