feat: add recsys-pipeline-architect skill (community)

skills/recsys-pipeline-architect/SKILL.md

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+---
+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.
+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](https://github.com/xai-org/x-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`