X Algorithm Skill

Optimize tweets and threads for the X (Twitter) For You feed algorithm.

How the Algorithm Works

The X For You feed uses a Grok-based Phoenix transformer that predicts engagement probabilities for each tweet. No hand-engineered features—it learns entirely from engagement patterns.

The Formula

Final Score = Σ (weight × P(action))

Each tweet gets scored by predicting the probability you'll take various actions, then weighting them.

What It Predicts

Positive signals (boost score):

Signal	What It Measures
`P(favorite)`	Likelihood of like
`P(reply)`	Likelihood of reply
`P(repost)`	Likelihood of repost
`P(quote)`	Likelihood of quote tweet
`P(click)`	Likelihood of clicking through
`P(profile_click)`	Likelihood of visiting author's profile
`P(video_view)`	Likelihood of watching video
`P(photo_expand)`	Likelihood of expanding image
`P(share)`	Likelihood of sharing externally
`P(dwell)`	Time spent reading/viewing
`P(follow_author)`	Likelihood of following

Negative signals (reduce score):

Signal	What It Measures
`P(not_interested)`	Likelihood of "Not interested"
`P(block_author)`	Likelihood of blocking
`P(mute_author)`	Likelihood of muting
`P(report)`	Likelihood of reporting

Additional Ranking Factors

Author Diversity: Repeated posts from same author get attenuated
Out-of-Network Boost: Discovered content (not from follows) gets special scoring
Candidate Isolation: Each tweet scored independently

Commands

/x-algorithm analyze [tweet]     # Score a tweet draft
/x-algorithm optimize [tweet]    # Suggest improvements
/x-algorithm signals             # Show all ranking signals
/x-algorithm hooks               # Generate high-engagement hooks

Usage

Analyzing a Tweet

/x-algorithm analyze "Just shipped a new feature"

ANALYSIS:
┌─────────────────────────────────────────────────┐
│  Tweet: "Just shipped a new feature"            │
├─────────────────────────────────────────────────┤
│  Predicted signals:                             │
│  ├─ P(favorite)      LOW   - no emotional hook  │
│  ├─ P(reply)         LOW   - no question/gap    │
│  ├─ P(repost)        LOW   - no value to share  │
│  ├─ P(quote)         LOW   - nothing to add to  │
│  ├─ P(dwell)         LOW   - too short          │
│  └─ P(not_interested) MED  - generic update     │
│                                                 │
│  Overall: WEAK                                  │
│                                                 │
│  Issues:                                        │
│  • No specificity (what feature? why care?)     │
│  • No emotional trigger                         │
│  • No reason to engage                          │
└─────────────────────────────────────────────────┘

Optimize? (yes/no)

Optimizing a Tweet

/x-algorithm optimize "Just shipped a new feature"

OPTIMIZED VERSIONS:

v1 (curiosity gap):
"The feature everyone asked for just shipped.

Took 6 months. Here's why it was worth the wait:"

v2 (social proof):
"1,247 people requested this feature.

Today we shipped it.

[screenshot]"

v3 (contrarian):
"Everyone said this feature was impossible.

We built it anyway.

Here's how:"

---

Which version, or iterate?

Optimization Principles

Maximize Positive Signals

For P(favorite) - likes:

Strong opinion or take
Relatable observation
Emotional resonance
Beautiful visually

For P(reply) - replies:

Ask a question
Create a knowledge gap
Be slightly wrong (people love to correct)
Request input

For P(repost) - reposts:

Provide shareable value (tips, insights)
Create "I wish I said that" moments
Make people look smart for sharing

For P(quote) - quotes:

Leave room for commentary
Take a stance others want to respond to
Share something people want to add context to

For P(dwell) - time on tweet:

Longer, readable content
Images that require study
Threads with substance
Videos

For P(follow) - new followers:

Demonstrate unique expertise
Show personality
Consistent topic/niche

Minimize Negative Signals

Avoid P(not_interested):

Don't be generic
Don't repeat what everyone says
Don't post off-topic

Avoid P(block/mute):

Don't be annoying
Don't spam
Don't be hostile
Don't engage in bad faith

Avoid P(report):

Don't violate ToS
Don't harass
Don't spread misinfo

High-Engagement Patterns

The Hook Patterns

1. CURIOSITY GAP
   "I spent 3 years learning [X]. Here's what I wish I knew:"

2. CONTRARIAN
   "Unpopular opinion: [hot take]"

3. STORY OPENER
   "In 2019 I was [relatable struggle]. Now I [impressive outcome]."

4. SPECIFIC NUMBER
   "I've [done X] 847 times. Here's what works:"

5. BEFORE/AFTER
   "I used to [common mistake]. Then I learned [insight]."

6. QUESTION
   "What's one thing you wish you learned earlier about [X]?"

Thread Structures That Work

LISTICLE:
"10 [things] about [topic]:"
→ High dwell, easy to repost individual tweets

BUILD-UP:
1. Hook
2. Context
3. Insight
4. Proof
5. Implication
6. CTA
→ Maximizes dwell across thread

STORY:
1. "It was 2AM when..."
2. Rising action
3. Crisis point
4. Resolution
5. Lesson
6. CTA
→ High engagement, emotional resonance

Visual Content

IMAGES:
- Screenshots > stock photos
- Before/after comparisons
- Data visualizations
- Behind-the-scenes

VIDEOS:
- Hook in first 1-3 seconds
- Subtitles (most watch muted)
- Native upload > links
- <2 min optimal

Integration with /content

When using /content thread or /content post, the X algorithm principles are automatically applied:

/content thread "our new pricing model"

Applying X algorithm optimization...
├─ Hook pattern: SPECIFIC NUMBER
├─ Thread structure: BUILD-UP
├─ Engagement triggers: curiosity, social proof
└─ Visual: screenshot recommendation

[generates thread with algorithm principles]

Author Diversity Consideration

The algorithm attenuates repeated authors. Posting strategy matters:

SUBOPTIMAL:
Post → Post → Post → Post (same hour)
Algorithm reduces later posts' reach

BETTER:
Post → [gap] → Post → [gap] → Post
Each post gets full scoring potential

Out-of-Network Discovery

To reach beyond your followers:

- Quote tweet popular accounts (your take on their content)
- Reply meaningfully to trending topics
- Create highly repostable content (others share to their network)
- Post content that generates quotes (your reach + quoter's reach)

Metrics to Watch

After posting, monitor:

Metric	What It Tells You
Impressions from For You	Algorithm reach
Impressions from profile	Direct followers
Engagement rate	Content quality signal
Quote:Repost ratio	How "discussable" content is
Reply quality	Community engagement depth

Source

Based on the open-sourced X algorithm: https://github.com/xai-org/x-algorithm

The algorithm is continuously updated. Check the repo for latest changes.

Search AI Tools

x-algorithm

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Metadata

SKILL.md