Agent Skills Hub

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.

training-data-curation

Guidelines for curating high-quality datasets for LLM post-training (SFT/DPO/RLHF), covering data formats, quality filtering, and collection strategies.

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.

manufacturing-failure-reason-codebook-normalization

Normalizes testing defect logs by correcting typos, abbreviations, and ambiguous descriptions based on product-specific codebooks and station validation.

language-learning

AI language tutor for personalized learning through conversation, grammar lessons, vocabulary drills, and flashcards. Supports 100+ languages including Spanish, French, Japanese, and Mandarin.

librarian

Fetch, index, and search developer documentation from GitHub and websites to provide AI agents with accurate, grounded, and version-specific code context.

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.

generate-image

Generate or edit images using AI models like FLUX and Gemini. Ideal for photos, illustrations, concept art, and visual assets, excluding technical diagrams and schematics.

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.

paper-reproduce

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

ReasoningBank Intelligence

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in AI agents.

scikit-learn

Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.