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Transfer Learning Adaptation Skill

adapting-transfer-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

Early Stopping Callback

early-stopping-callback

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Gradient Clipping Helper

gradient-clipping-helper

jeremylongshore/claude-code-plugins-plus-skills

Gradient Clipping Helper automates ML training work by guiding gradient clipping workflows, generating production-ready code and configs, and validating outputs for PyTorch, TensorFlow, and related pipelines.

Learning Rate Scheduler

learning-rate-scheduler

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

Production ML Engineering and MLOps

sickn33/antigravity-awesome-skills

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

MLflow Lifecycle Platform

Orchestra-Research/AI-Research-SKILLs

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Ray Data Processing

Orchestra-Research/AI-Research-SKILLs

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

Ray Train Orchestration

Orchestra-Research/AI-Research-SKILLs

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

TensorBoard Visualization Toolkit

Orchestra-Research/AI-Research-SKILLs

TensorBoard delivers a unified interface to track training metrics, debug network behavior, compare experiments, inspect model graphs, and profile performance for PyTorch or TensorFlow workflows.

Tensorflow Model Trainer

tensorflow-model-trainer

jeremylongshore/claude-code-plugins-plus-skills

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Tensorflow SavedModel Creator

tensorflow-savedmodel-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

TensorFlow Serving Setup

tensorflow-serving-setup

jeremylongshore/claude-code-plugins-plus-skills

Guides TensorFlow Serving setup with step-by-step ML deployment orchestration, enforcing best practices, ready-to-run configurations, and output validation for production inference services.

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