Agent skill · product strategy · affaan-m
recsys-pipeline-architect
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks "the top K items for a (user, context)" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
Why this skill is useful
Provides a structured framework for building recommendation systems that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-06. 238,342 stars on the source repository.
What this skill does
recsys-pipeline-architect 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: <https://github.com/mturac/recsys-pipeline-architect> When to Use 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" When NOT to Use Model architecture work (transformer design, two-tower retrieval, embedding training) — this skill is plumbing around the model, not the model itself Pure ML training pipelines — the scoring function is the user's responsibility Operating a deployed pipeline (monitoring, autoscaling) — out of scope The six-stage framework # Stage Job Parallel? --- --- --- --- 1 Source Fetch candidates from one or more origins Yes — multiple sources run in parallel 2 Hydrator Enrich each candidate with metadata needed for filtering and scoring Yes — independent hydrators run in parallel 3 Filter Drop candidates that should never be shown (blocked, expired, duplicate, ineligible) Sequential — each filter sees fewer items 4 Scorer Assign each surviving candidate one or more scores Sequential — later scorers see earlier scores 5 Selector Sort by final score, return …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills affaan-m/recsys-pipeline-architect