recsys-pipeline-architect

name recsys-pipeline-architect description 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. metadata {"origin":"community"} 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 top K Single op 6 SideEffect Cache served IDs, log impressions, emit events, update counters Async — must never block the response Why this exact order Sources before hydration: know what candidates exist before paying to enrich them Hydration before filtering: many filters need metadata the source did not provide Filtering before scoring: scoring is the expensive stage; drop the ineligible first Scorer chain (not single scorer): real systems compose ML scoring + diversity reranking + business rules Selector after scoring: keeps scoring deterministic and cacheable SideEffects last and async: side effects must never block the user response Workflow when invoked Walk the user through these eight steps: Clarify the use case (one round, three questions): items being ranked? input context? language/runtime? Identify the candidate sources : usually in-network (followed/owned/subscribed) + out-of-network (ML retrieval / trending / similar-to-liked) List required hydrations : for each filter and scorer, what data does it need that the source did not provide? List the filters : duplicate, self, age, block/mute, previously-served, eligibility. Order matters — cheap before expensive. Design the scorer chain : primary (ML) → combiner (multi-action with weights) → diversity → business rules Selector : sort descending by final score, take top K (or stratified mix for in-network/out-of-network) SideEffects : cache served IDs, emit impression events, update counters, log analytics — all fire-and-forget Generate the scaffold in the user's stack Key trade-offs to surface (don't default silently) 1. Single score vs multi-action prediction Single score : train one model to predict relevance. To change behavior → retrain. Multi-action : predict P(action) for many actions (read, like, share, skip, report), combine with weights at serving time. To change behavior → change weights. No retraining. The X For You system uses multi-action with both positive and negative weights. Recommend multi-action when the user expects to tune frequently. 2. Candidate isolation in scoring Isolated : each candidate scored independently. Deterministic, cacheable. Joint : candidates attend to each other during scoring (e.g., transformer over batch). More expressive but non-deterministic across batches. Default to isolation. Joint only when there's a specific reason (e.g., explicit batch-aware diversity). 3. Online vs offline Request-time (online) : pipeline runs on each request. Latency budget: 100–300ms. Default. Pre-computed (offline batch) : pipeline runs periodically, results cached. Lower latency, lower freshness. Hybrid : candidate retrieval offline, ranking online. Hard rules Do not invent benchmark numbers. "How much faster?" → "depends on workload, run it yourself." Attribution discipline. When the pattern is referenced, attribute as "popularized by xAI's open-sourced For You algorithm" / github.com/xai-org/x-algorithm (Apache 2.0). No trademark use. Do not name the user's artifact "X-like" or use "For You" branding. Pattern is free; brand is not. Suggested naming: "candidate pipeline", "feed pipeline", "ranking pipeline", "recsys pipeline". Surface trade-offs. Multi-action vs single, isolation vs joint, online vs offline — never default silently. The generated scaffold must run. No pseudocode passing as code. Filter order matters. Cheap before expensive. Universal before user-specific. Side effects never block. Wrap in fire-and-forget patterns (goroutines / promises without await / asyncio tasks). Anti-Patterns Scoring before filtering (wastes compute on candidates that will be dropped anyway) Synchronous side effects (cache writes / impression emits blocking the response) A single "relevance" score when the product needs to tune for multiple objectives (engagement vs safety vs diversity vs ads) Joint scoring as default (non-deterministic, harder to cache, doesn't compose with reranking stages) Generating pseudocode "for illustration" — the scaffold must actually run Upstream contents The upstream repository at https://github.com/mturac/recsys-pipeline-architect ships: Full SKILL.md with the complete 8-step workflow 5 load-on-demand reference docs: interfaces in 4 languages (TS/Go/Python/Rust), multi-action scoring pattern, candidate isolation, filter cookbook (12 patterns), scorer cookbook (weighted sum, MMR, diversity penalty, position debiasing) 3 runnable example scaffolds, every one green on its test suite: Strapi v5 plugin (TypeScript / Jest — 3/3 pass) Zentra-compatible pipeline (Go with generics — 3/3 pass) PMAI task prioritizer (Python / FastAPI / pytest — 3/3 pass) v0.1.0 release tagged MIT license; pattern attributed to xAI X For You algorithm (Apache 2.0) Install via skills.sh: npx skills add mturac/recsys-pipeline-architect