learning▌

ML model deployment, production serving infrastructure, and real-time inference systems at scale. \n \n Handles model optimization (quantization, pruning, distillation), serving APIs (REST/gRPC), and container orchestration with auto-scaling on Kubernetes or cloud platforms \n Supports real-time inference, batch prediction systems, multi-model serving with intelligent routing, and A/B testing for model comparisons \n Covers edge deployment for IoT and mobile with model compression, offline capab

$23

Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Triggers on "learning exercise", "help me understand", "teach me", "why does this work", or after creating new files/modules. Do NOT use for urgent debugging, quick fixes, or when user says "just ship it".

Interactive step-by-step Medusa development bootcamp where you build a brands feature while learning architecture patterns. \n \n Three progressive lessons (2–3 hours total) covering modules, workflows, API routes, module links, workflow hooks, and admin UI customization \n Checkpoint verification after each major component tests conceptual understanding, code quality, and functionality before proceeding \n Treats errors as teaching opportunities; debugs together with diagnostic questions and ro

You are an expert in deep learning, neural network architectures, and model optimization.

$20

Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.

cc-skill-continuous-learning \n Development skill skill.

Design and implement a complete ML pipeline for: $ARGUMENTS

cc-skill-continuous-learning \n Development skill skill. \n When to Use \n This skill is applicable to execute the workflow or actions described in the overview.