X Open Source Algorithm Skill

X Algorithm Optimizer

Optimize X/Twitter content using the actual January 2026 open source algorithm from xai-org/x-algorithm.

Source Material

This skill is built from three sources:

1. The actual algorithm code (Jan 2026): ~/Documents/Work/Social Media Guides/Platform Guides/x-algorithm-2026/
2. 2026 practical guide: ~/Documents/Work/David Brand/x-twitter-algorithm-2026.md
3. David Cyrus voice profile: ~/Documents/Work/David Brand/david-cyrus-voice-profile.md (for @mdavidcyrus posts)

Read references/phoenix-scoring.md for the full scoring model details. Read references/algorithm-signals.md for the practical engagement hierarchy and tactics.

How the Algorithm Actually Works (Jan 2026)

X's "For You" feed is powered by four components:

1. Home Mixer (orchestration): assembles the feed through a pipeline of sources, hydrators, filters, scorers, and selectors
2. Thunder (in-network): serves recent posts from accounts you follow via in-memory store
3. Phoenix (ranking + retrieval): Grok-based transformer that predicts engagement probabilities
4. Candidate Pipeline (framework): reusable pipeline stages that run in parallel

The critical path for content creators: Phoenix predicts 19 engagement probabilities for every post, and a Weighted Scorer combines them into a single ranking score.

The Phoenix Scoring Model

Phoenix predicts these engagement probabilities for every candidate post:

Positive signals (higher = more reach):

| Signal | What triggers it | Impact | |--------|-----------------|--------| | P(favorite) | Likes | Primary distribution signal | | P(reply) | Replies to the post | Triggers +75 author engagement bonus | | P(repost) | Retweets | Strong amplification signal | | P(quote) | Quote tweets | Engaged discussion signal | | P(click) | Link/content clicks | Interest signal | | P(profileclick) | Clicking author's profile | Curiosity/authority signal | | P(videoqualityview) | Watching video >50% | Only applies to video posts | | P(share) | Share button usage | High-intent distribution | | P(shareviadm) | Sharing via DM | Personal recommendation signal | | P(shareviacopylink) | Copying link to share | Off-platform sharing intent | | P(dwell) | Stopping to read/view | Attention signal | | P(dwelltime) | How long they stay (continuous) | Depth of engagement | | P(followauthor) | Following the author | Strongest authority signal |

Negative signals (higher = penalized):

| Signal | What triggers it | Impact | |--------|-----------------|--------| | P(notinterested) | "Not interested" button | Moderate penalty | | P(blockauthor) | Blocking the author | Severe penalty | | P(mute_author) | Muting the author | Significant penalty | | P(report) | Reporting the post | Most severe penalty |

The weighted score formula:

Final Score = sum(weight_i * P(action_i))

Positive actions have positive weights. Negative actions have negative weights.

The exact weight values are excluded from the open source release (in a private params module), but the 2026 practical data gives us the observed hierarchy:

• Likes: +30 points
• Retweets: +20 points
• Reply with author response: +75 bonus
• Reply alone: +13.5

How to Use This Skill

Step 1: Determine Format

Based on the 2026 algorithm data, content formats rank:

1. X Articles (algorithm-boosted, $1M monthly prize, highest dwell time)
2. Video thread (video + 3-5 tweets, 0.42% engagement rate)
3. Video single post (5x text engagement)
4. Image/GIF thread (150% more interactions than text)
5. Text thread (3x single tweet engagement)
6. Image/GIF single (0.08% engagement)
7. Text single (0.1% engagement)
8. Link post Premium (0.25%, heavily suppressed)
9. Link post non-Premium (0% engagement, invisible)

Decision tree:

• Deep analysis, 1000+ words? -> X Article
• Punchy take, 5-10 points? -> Thread (7 tweets optimal)
• Quick insight, single idea? -> Single tweet with image/video
• Linking to external content? -> Native content first, link in reply only

Step 2: Analyze the Draft

For each draft post, evaluate against the Phoenix scoring signals:

Will this trigger positive signals?

• [ ] Likely to get likes? (favorite_score) -> clear value, quotable insight
• [ ] Likely to get replies? (reply_score) -> provocative question, contrarian take, reply hook
• [ ] Likely to get retweets? (retweet_score) -> shareable, makes the sharer look smart
• [ ] Likely to get clicks? (click_score) -> curiosity gap, "what happened next"
• [ ] Likely to increase dwell time? (dwell_score) -> thread format, visual content, depth
• [ ] Likely to trigger profile visits? (profileclickscore) -> establishing authority, unique perspective
• [ ] Likely to trigger follows? (followauthorscore) -> demonstrating ongoing value

Will this trigger negative signals?

• [ ] Could trigger "not interested"? -> off-topic for your audience, unclear value
• [ ] Could trigger mutes? -> posting too frequently, repetitive content
• [ ] Could trigger blocks? -> aggressive, offensive, spam-like behavior

Step 3: Optimize for the Algorithm

The 30-Minute Rule: The algorithm evaluates engagement velocity in the first 30 minutes. Front-load your best content.

Premium multipliers:

• In-network (followers): 4x visibility
• Out-of-network (non-followers): 2x visibility
• Overall: 10x reach advantage (Buffer analysis of 18.8M posts)

Thread optimization:

• First tweet: no number, use 🧵 emoji
• Start numbering from tweet 2: "2/7", "3/7"
• Each tweet under 200 characters (250 max)
• Line breaks between ideas
• 1-2 hashtags max, in final tweet only
• Links in final tweet or separate reply
• Optimal length: 7 tweets (5-10 range)

What kills reach:

• External links in main post (even Premium: 0.25% engagement)
• 3+ hashtags (engagement drops)
• No visual content (5x penalty vs video)
• Text-only posts (lowest format, 0.1%)
• Slow first-30-minute engagement

Step 4: Score the Post

Rate each draft on a simplified Phoenix-aligned scale:

| Category | Score 1-10 | Weight | |----------|-----------|--------| | Reply potential (will people respond?) | | 30% | | Repost potential (will people share?) | | 25% | | Dwell time (will people stop and read?) | | 20% | | Like potential (quick positive reaction?) | | 15% | | Negative risk (could this backfire?) | _ | 10% |

**Weighted Score = (Reply 0.3) + (Repost 0.25) + (Dwell 0.2) + (Like 0.15) - (Negative 0.1)*

Target: 7.0+ for posting. Below 6.0, rewrite.

Step 5: Rewrite Suggestions

When suggesting rewrites, explain which Phoenix signal you're targeting:

Example:

• Original: "Claude's 1M context window is now available."
• Problem: Low replyscore (no reason to respond), low dwellscore (nothing to read)
• Rewrite: "Anthropic just gave Claude a million-token context window. I turned mine off. Here's why. 🧵"
• Why: Opens curiosity gap (clickscore), demands engagement (replyscore), signals thread (dwell_score)

Voice Reference

For @mdavidcyrus posts, read ~/Documents/Work/David Brand/david-cyrus-voice-profile.md and apply:

• Direct, pragmatic, builder, contrarian
• Executive voice, not content creator
• Lead with insight, not story
• No em dashes, no AI-tell words
• No engagement bait ("Agree?", "Thoughts?")
• Statement energy, end on declarations

Anti-patterns (from the algorithm code)

The algorithm has explicit negative signal predictions. These actions cause the scoring model to suppress your content:

1. P(not_interested) increases when: content is off-topic for your community (SimClusters mismatch), repetitive themes, unclear value proposition
2. P(block_author) increases when: aggressive tone, spam-like posting frequency, controversial content without substance
3. P(mute_author) increases when: over-posting (more than 5-7 daily), thread-bombing, excessive self-promotion
4. P(report) increases when: misleading claims, harassment, harmful content

The Author Diversity Scorer also penalizes seeing the same author too many times in one feed session. Each subsequent post from the same author gets a decaying multiplier. Posting quality over quantity matters.

Output Format

When optimizing a post, provide:

1. Format recommendation (article / thread / single + why)
2. Phoenix signal analysis (which signals this will trigger, which it won't)
3. Score (weighted 1-10 scale above)
4. Specific rewrites (with signal rationale for each change)
5. Posting strategy (timing, first-30-min plan, reply strategy)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: attainmentlabs
• Source: attainmentlabs/x-open-source-algorithm-skill
• License: MIT

Install and usage instructions live in the source repository linked above.