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:

1. Clarify the use case (one round, three questions): items being ranked? input context? language/runtime?
2. Identify the candidate sources: usually in-network (followed/owned/subscribed) + out-of-network (ML retrieval / trending / similar-to-liked)
3. List required hydrations: for each filter and scorer, what data does it need that the source did not provide?
4. List the filters: duplicate, self, age, block/mute, previously-served, eligibility. Order matters — cheap before expensive.
5. Design the scorer chain: primary (ML) → combiner (multi-action with weights) → diversity → business rules
6. Selector: sort descending by final score, take top K (or stratified mix for in-network/out-of-network)
7. SideEffects: cache served IDs, emit impression events, update counters, log analytics — all fire-and-forget
8. 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

1. Do not invent benchmark numbers. "How much faster?" → "depends on workload, run it yourself."
2. 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).
3. 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".
4. Surface trade-offs. Multi-action vs single, isolation vs joint, online vs offline — never default silently.
5. The generated scaffold must run. No pseudocode passing as code.
6. Filter order matters. Cheap before expensive. Universal before user-specific.
7. 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