recsys-pipeline-architect
Designs composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework
Data & Analyticsfeed-algorithmfor-you-feedpersonalizationpipeline-architecturerag-rerankerrankingrecommender-systemrecsys
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):
- What are the items being ranked? (posts, products, tasks, alerts, documents...
Subscribers only
The full skill, its 1 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | mturac/recsys-pipeline-architect |
|---|---|
| License | MIT |
| Risk label | safe ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md |
| Added | 2026-05-16 |
Related skills
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
clarity-gate
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
embedding-strategies
Guide to selecting and optimizing embedding models for vector search applications.
exa-search
Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).
llm-app-patterns
Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.
local-llm-expert
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.