x-algo-engagement

Name: x-algo-engagement - Agent Skill
Rating: 3.0 (6 reviews)
Author: CloudAI-X

Reference for X algorithm engagement types and signals. Use when analyzing engagement metrics, action predictions, or understanding what signals the algorithm tracks.

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CLI Install

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Use the skills CLI to install this skill with one command. Auto-detects all installed AI assistants.

Method 1 - skills CLI

npx skills i CloudAI-X/x-algo-skills/x-algo-engagement

Method 2 - openskills (supports sync & update)

npx openskills install CloudAI-X/x-algo-skills

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Installation Path

Download and extract to one of the following locations:

~/.claude/skills/x-algo-engagement/

SKILL.md

Skill Instructions

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Claude Sonnet 4.5

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X Algorithm Engagement Signals

The X recommendation algorithm tracks 18 engagement action types plus 1 continuous metric. These are predicted by the Phoenix ML model and used to calculate weighted scores.

PhoenixScores Struct

Defined in home-mixer/candidate_pipeline/candidate.rs:

pub struct PhoenixScores {
    // Positive engagement signals
    pub favorite_score: Option<f64>,
    pub reply_score: Option<f64>,
    pub retweet_score: Option<f64>,
    pub quote_score: Option<f64>,
    pub share_score: Option<f64>,
    pub share_via_dm_score: Option<f64>,
    pub share_via_copy_link_score: Option<f64>,
    pub follow_author_score: Option<f64>,
 
    // Engagement metrics
    pub photo_expand_score: Option<f64>,
    pub click_score: Option<f64>,
    pub profile_click_score: Option<f64>,
    pub vqv_score: Option<f64>,              // Video Quality View
    pub dwell_score: Option<f64>,
    pub quoted_click_score: Option<f64>,
 
    // Negative signals
    pub not_interested_score: Option<f64>,
    pub block_author_score: Option<f64>,
    pub mute_author_score: Option<f64>,
    pub report_score: Option<f64>,
 
    // Continuous actions
    pub dwell_time: Option<f64>,
}

Action Types by Category

Positive Engagement (High Value)

Action	Proto Name	Description
Favorite	`ServerTweetFav`	User likes the post
Reply	`ServerTweetReply`	User replies to the post
Retweet	`ServerTweetRetweet`	User reposts without comment
Quote	`ServerTweetQuote`	User reposts with their own comment
Follow Author

Action	Proto Name	Description
Share	`ClientTweetShare`	Generic share action
Share via DM	`ClientTweetClickSendViaDirectMessage`	User shares via direct message
Share via Copy Link	`ClientTweetShareViaCopyLink`	User copies link to share externally

Engagement Metrics

Action	Proto Name	Description
Photo Expand	`ClientTweetPhotoExpand`	User expands photo to view
Click	`ClientTweetClick`	User clicks on the post
Profile Click	`ClientTweetClickProfile`	User clicks author's profile
VQV	`ClientTweetVideoQualityView`	Video Quality View - user watches video for meaningful duration
Dwell

Negative Signals

Action	Proto Name	Description
Not Interested	`ClientTweetNotInterestedIn`	User marks as not interested
Block Author	`ClientTweetBlockAuthor`	User blocks the author
Mute Author	`ClientTweetMuteAuthor`	User mutes the author
Report	`ClientTweetReport`	User reports the post

Continuous Actions

Action	Proto Name	Description
Dwell Time	`DwellTime`	Continuous value: seconds spent viewing post

How Scores Are Obtained

The PhoenixScorer (home-mixer/scorers/phoenix_scorer.rs) calls the Phoenix prediction service:

Input: User history + candidate posts
Output: Log probabilities for each action type per candidate
Conversion: probability = exp(log_prob)

fn extract_phoenix_scores(&self, p: &ActionPredictions) -> PhoenixScores {
    PhoenixScores {
        favorite_score: p.get(ActionName::ServerTweetFav),
        reply_score: p.get(ActionName::ServerTweetReply),

Signal Interpretation

Scores are probabilities (0.0 to 1.0): P(user takes action | user sees post)
Higher = more likely: A favorite_score of 0.15 means 15% predicted chance of like
Negative signals have negative weights: High report_score reduces overall ranking
VQV requires minimum video duration: Only applies to videos > MIN_VIDEO_DURATION_MS

/x-algo-scoring - How these signals are combined into a weighted score
/x-algo-ml - How Phoenix model predicts these probabilities