X Algo Engagement

Name: X Algo Engagement
Author: cloudai-x

cloudai-x/x-algo-skills

15 installs
11 repo stars
Updated January 20, 2026
cloudai-x/x-algo-skills

Reference for the X recommendation algorithm's engagement action types and signals tracked by the Phoenix model for scoring.

About

Documents the 18 engagement action types plus continuous metrics the X algorithm tracks via the PhoenixScores struct, including positive, metric, and negative signals. A developer or growth analyst uses it when analyzing engagement metrics and action predictions.

PhoenixScores struct enumerates favorite, reply, retweet, and negative signals
Distinguishes positive engagement, metrics, and negative signals

X Algo Engagement by the numbers

15 all-time installs (skills.sh)
Ranked #1,496 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
Data as of Jul 29, 2026 (Skillselion catalog sync)

npx skills add https://github.com/cloudai-x/x-algo-skills --skill x-algo-engagement

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Installs	15
repo stars	★ 11
Last updated	January 20, 2026
Repository	cloudai-x/x-algo-skills ↗

What it does

Reference for the X recommendation algorithm's engagement action types and signals tracked by the Phoenix model for scoring.

Files

SKILL.mdMarkdownGitHub ↗

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	`ClientTweetFollowAuthor`	User follows the post's author

Sharing Actions

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	`ClientTweetRecapDwelled`	User dwells (pauses) on the post
Quoted Click	`ClientQuotedTweetClick`	User clicks on a quoted post

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:

1. Input: User history + candidate posts 2. Output: Log probabilities for each action type per candidate 3. 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),
        retweet_score: p.get(ActionName::ServerTweetRetweet),
        // ... maps each action to its probability
    }
}

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