recsys-pipeline-architect

原文涵盖的主题

• recsys-pipeline-architect
• When to Use
• When NOT to Use
• The six-stage framework
• Why this exact order
• Workflow when invoked
• Key trade-offs to surface (don't default silently)
• 1. Single score vs multi-action prediction

A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. It encodes the six-stage pattern — Source → Hydrator → Filter → Scorer → Selector → SideEffect — popularized by xAI's open-sourced For You algorithm (Apache 2.0). This skill is an independent reimplementation of the pattern (MIT) — no code copied from the original. Upstream: User wants to build any system that picks "the top K items for a user/context" User asks "how should I rank X" or describes a feed/personalization problem User has a scoring function and needs the pipeline plumbing around it User wants to migrate from a single relevance score to multi-action prediction with tunable weights User is wrapping an LLM/ML scorer and needs filters, hydrators, side-effects, and a runnable scaffold in their stack (TypeScript / Go / Python) Triggers: "recommendation system", "feed algorithm", "ranking pipeline", "for you feed", "candidate pipeline", "content recommender", "pipeline architecture for recsys", "RAG retrieval reranker" Model architecture work (transformer design, two-tower retrieval, embedding training) — this skill is plumbing around the model, not the model itself Pure ML trainin

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