A recommendation system picks the best items for each person. This skill helps you build such systems using a six stage pipeline. The stages are Source, Hydrator, Filter, Scorer, Selector, and SideEffect. This approach works for feeds, search results, notifications, and more.
You start by finding candidates from one or more sources. Then you enrich them with extra data. Next you remove items that don't qualify. After that you score each remaining item. You pick the top ones and finally run background tasks like logging. Each stage has a clear job and runs in a specific order to save time and money.
Anyone building a personalized list of items can use this skill. It works with any programming language or runtime. The pattern is simple and reusable. You do not need complex machine learning to get started.
Global
mkdir -p ~/.claude/skills/recsys-pipeline-architectProject
mkdir -p .claude/skills/recsys-pipeline-architectSource Repository
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