# X Dev Engineering > Use this skill when implementing new features or debugging the core "For You" pipeline. The system has shifted from legacy heuristics toward a unified **Rust-based** pipeline powered by the **Phoenix** transformer (Grok). - Skill: `tomevault-io/x-dev-engineering` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/x-dev-engineering` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/x-dev-engineering/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/x-dev-engineering --- # X Development Engineering Technical expertise for developing and extending the X recommendation engine, focusing on the Grok-powered Phoenix model, Rust candidate pipelines, and the removal of hand-engineered features. ## Context The modern X algorithm prioritizes a "No Hand-Engineered Features" philosophy. Relevance is learned directly from user engagement sequences using a transformer architecture. For technical deep-dives, see: - [The Rust Candidate Pipeline](./references/rust-pipeline.md) - [Phoenix (Grok) Model Scoring](./references/phoenix-scoring.md) - [Visibility & Safety Engineering](./references/visibility-safety.md) ## What it does * **Implements Grok-Native Components:** Guidance on integrating with the Phoenix transformer for multi-action prediction (Like, Repost, Reply, etc.). * **Architects Candidate Sources:** Logic for the two primary retrieval engines: **Thunder** (In-Network) and **Phoenix Retrieval** (Out-of-Network). * **Enforces Pipeline Order:** Ensures components follow the strict sequence: Query Hydration -> Sourcing -> Hydration -> Pre-Scoring Filters -> Scoring -> Selection. * **Optimizes for Parallelism:** Leverages Rust's async/await to execute independent hydration and sourcing tasks concurrently. ## Guidelines * **Candidate Isolation:** During scoring, ensure candidates cannot "attend" to each other in the transformer. This maintains score consistency and enables batching. * **No Heuristics:** Avoid adding manual "if/then" rules for relevance; the Phoenix model should handle weightings through learned engagement probabilities. * **Hydration Efficiency:** Fetch only necessary metadata (text, media, author status) during the Candidate Hydration stage after initial sourcing to minimize I/O. * **Safety First:** All content must pass through `VisibilityLib` (Rust) for legal compliance and safety filtering before reaching the user. ## Example Trigger Prompts * "/gen-thrift new ranking signal" * "/gen-thrift scaffold Rust Filter trait" * "Add a new engagement signal to Phoenix prediction" * "Show Rust implementation for Thunder candidate source" * "Explain 'No Hand-Engineered Features' in ranking update" * "How is candidate isolation applied in transformer attention?" --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/elemontcapital) — claim your Tome and manage your conversions.