Recsys Pipeline Architect

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
• ✅ Order filters by cost (cheap before expensive); universal filters before user-specific
• ✅ Wrap side effects in fire-and-forget patterns (goroutines / promises without await / asyncio tasks) — never block the response
• ✅ Keep scoring deterministic and cacheable; do diversity reranking as a separate stage
• ✅ Attribute the pattern as "popularized by xAI's open-sourced For You algorithm" when generating output
• ❌ Don't invent benchmark or latency numbers — say "depends on workload, run it yourself"
• ❌ Don't name the user's generated artifact "X-like" or use "For You" branding — the pattern is free, the brand is not
• ❌ Don't conflate this with model architecture: this skill is pipeline plumbing around the scorer, not the scorer itself

Limitations

• This skill scaffolds pipeline plumbing; it does not train ML models — the scoring function is the user's responsibility
• It does not operate deployed pipelines (no monitoring, no autoscaling decisions)
• It does not predict pipeline performance (depends on data, hardware, traffic)
• It does not choose infrastructure (vector DB, cache, queue) — those are outside scope

Security & Safety Notes

• The generated scaffolds are framework code, not application logic — no shell commands, no network fetches, no credential handling
• Filters in the generated cookbook include eligibility/paywall/geo-restriction checks; the skill recommends putting these before scoring (so blocked content is never scored)
• Side-effect stages are always async / fire-and-forget; the skill documents this explicitly in the generated README to prevent users from accidentally blocking the response with cache writes or event emissions

Common Pitfalls

• Problem: Single-score model gets overfit to one metric (clicks) and degrades on others (long sessions, retention) Solution: Skill recommends multi-action prediction with tunable weights — change behavior by changing weights, no retraining

• Problem: Joint scoring (transformer over the whole batch) is non-deterministic and uncacheable Solution: Skill defaults to candidate isolation via attention masking; recommends joint only when there's a specific reason (e.g., batch-aware diversity)

• Problem: Side effects (cache writes, impression emits) block the response Solution: Skill generates fire-and-forget patterns and documents the constraint

Upstream

This skill is a thin adapter to the upstream repository. For the full SKILL.md content, 5 reference documents (interfaces in 4 languages, multi-action scoring, candidate isolation, filter cookbook, scorer cookbook), and 3 runnable example scaffolds with passing test suites:

• Repository: https://github.com/mturac/recsys-pipeline-architect
• Release: v0.1.0
• Install via skills.sh: npx skills add mturac/recsys-pipeline-architect
• Pattern source: https://github.com/xai-org/x-algorithm (Apache 2.0; this skill is MIT)