# X Experimental Ops > Use this skill when reasoning about how the algorithm is tuned, why different users see different behaviors, or how "Success" is defined by X's engineering team. - Skill: `tomevault-io/x-experimental-ops` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/x-experimental-ops` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/x-experimental-ops/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/x-experimental-ops --- # X Experimental Ops Knowledge of X's A/B testing infrastructure (DuckDuckGoose) and the metrics used to measure algorithmic success. ## Context The algorithm is never "finished." It is a living system managed by **DuckDuckGoose (DDG)**, X's internal experimentation platform. Every change to a weight or a filter is first tested on a small percentage of the user base. - [DuckDuckGoose (A/B Testing)](./references/ab-testing-framework.md) - [Metrics & Alignment](./references/metrics-alignment.md) ## What it does * **Explains Bucketing:** * Details the mechanics of **DuckDuckGoose**, X's internal experimentation platform that uses salt-based consistent hashing to deterministically assign users to "Control" or "Treatment" variants. * Ensures "sticky" assignments so a user's experience remains consistent across sessions while maintaining statistically sound percentage-based rollouts (e.g., 1%, 5%, or 10% cohorts). * **Decodes Success Metrics:** * Breaks down the **"Unregretted User Minutes" (UUM)** North Star metric, which prioritizes high-value time spent (replies, likes, and deep reads) over passive scrolling or "clickbait" interactions that lead to user regret. * Analyzes how experimental changes impact the Multi-Task Learning (MTL) "heads" to ensure a boost in one engagement signal (like Retweets) doesn't negatively correlate with platform health or retention. * **Analyzes Feature Flags:** * Identifies how the system uses **Dynamic Configuration** and **Feature Gates** to toggle ranking logic or retrieval sources on and off for specific cohorts in real-time. * Explains the "Kill Switch" architecture that allows engineers to instantly roll back a new algorithmic feature if it causes a spike in latency or negative feedback without requiring a full code redeployment. ## Example Trigger Prompts * "/run-experiment salt-based hashing for user buckets" * "/run-experiment Unregretted User Minutes vs dwell time" * "Trace feature flag logic for latest Grok retrieval test" * "Show holdout group parameters for current Heavy Ranker A/B" * "Compare control vs variant metrics for feed engagement test" * "Explain how a new signal is staged in an experiment pipeline" --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/elemontcapital) — claim your Tome and manage your conversions.