Supporting material availability

This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:

• <support-root>/references/interfaces.md

Obtain the required material from the declared upstream source, or from a user-provided installation of that source. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.

recsys-pipeline-architect

Overview

A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. It encodes the six-stage Source → Hydrator → Filter → Scorer → Selector → SideEffect framework popularized by xAI's open-sourced For You algorithm (Apache 2.0). This skill is an independent reimplementation of the pattern — no code is copied from the original — licensed MIT. Use it whenever you need "the top K items for a (user, context)": social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.

When to Use This Skill

• Use when the user wants to build any system that picks "the top K items for a user/context"
• Use when the user asks "how should I rank X" or describes a feed/personalization problem
• Use when the user has a scoring function and needs the pipeline plumbing around it
• Use when the user wants to migrate from a single relevance score to multi-action prediction with tunable weights
• Use when the user is wrapping an LLM/ML scorer and needs filters, hydrators, side-effects, and a runnable scaffold in their stack (TypeScript / Go / Python)

How It Works

Step 1: Clarify the use case

Ask the user three questions (only what is missing):

1. What are the items being ranked? (posts, products, tasks, alerts, documents...)
2. What is the input context? (user ID, search query, current document, time window...)
3. What language / runtime? (TypeScript/Node, Go, Python, Rust...)

Step 2: Walk the eight steps of the spec

The full SKILL walks through: clarify use case → identify candidate sources → list required hydrations → list filters → design scorer chain → selector → side effects → generate scaffold. Each step surfaces the architectural trade-offs (multi-action vs single-score, candidate isolation vs joint scoring, online vs offline batch) so the user makes them explicitly rather than defaulting silently.

Step 3: Emit a runnable scaffold

The upstream repository ships three runnable example scaffolds — every one green on its test suite:

• Strapi v5 plugin (TypeScript, Jest, 3/3 pass) — adds GET /api/feed/for-you with multi-action scoring and author diversity
• Zentra-compatible pipeline (Go with generics, 3/3 pass) — engine.Module-compatible, standalone-usable
• PMAI task prioritizer (Python / FastAPI / pytest, 3/3 pass) — GET /tasks/next?user_id=42&limit=10

When the user's stack doesn't match, the skill generates from scratch following the interface definitions in references/interfaces.md (TypeScript, Go, Python, Rust).

Examples

Example 1: Strapi content feed

User: "I'm running a Strapi v5 instance with 50k articles. I want a 'for you' feed personalized to each logged-in user based on their reading history."

Skill walks through the 8 steps, generates a Strapi plugin scaffold using the Strapi example as the template.

Example 2: RAG retrieval reranker

User: "My RAG returns top-50 chunks from a vector DB. I want to rerank them with a more expensive scorer and return top-5."

Skill recognizes this as a single-source pipeline with a scorer chain (cheap retrieval + expensive rerank). Generates a Python async pipeline.

Example 3: Notification triage

User: "We send too many notifications. I want a daily digest that picks the top 10 from the last 24h queue."

Skill identifies this as an offline-batch pipeline. Generates a scheduled job scaffold.

Best Practices

• ✅ Surface the multi-action vs single-score trade-off explicitly — don't default silently

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