Agent Skill
2/7/2026

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.

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elemontcapital
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npx skills add ElemontCapital/x-algorithm-skills

SKILL.md

Namex-experimental-ops
DescriptionUse 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.

name: x-experimental-ops description: 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. version: 1.0.0 license: MIT

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.

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"
Skills Info
Original Name:x-experimental-opsAuthor:elemontcapital