name: twitter-algorithm-optimizer
description: Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
license: AGPL-3.0 (referencing Twitter's algorithm source)

Twitter Algorithm Optimizer

When to Use This Skill

Use this skill when you need to:
- Optimize tweet drafts for maximum reach and engagement
- Understand why a tweet might not perform well algorithmically
- Rewrite tweets to align with Twitter's ranking mechanisms
- Improve content strategy based on the actual ranking algorithms
- Debug underperforming content and increase visibility
- Maximize engagement signals that Twitter's algorithms track

What This Skill Does

1. Analyzes tweets against Twitter's core recommendation algorithms
2. Identifies optimization opportunities based on engagement signals
3. Rewrites and edits tweets to improve algorithmic ranking
4. Explains the "why" behind recommendations using algorithm insights
5. Applies Real-graph, SimClusters, and TwHIN principles to content strategy
6. Provides engagement-boosting tactics grounded in Twitter's actual systems

How It Works: Twitter's Algorithm Architecture

Twitter's recommendation system uses multiple interconnected models:

Core Ranking Models

Real-graph: Predicts interaction likelihood between users
- Determines if your followers will engage with your content
- Affects how widely Twitter shows your tweet to others
- Key signal: Will followers like, reply, or retweet this?

SimClusters: Community detection with sparse embeddings
- Identifies communities of users with similar interests
- Determines if your tweet resonates within specific communities
- Key strategy: Make content that appeals to tight communities who will engage

TwHIN: Knowledge graph embeddings for users and posts
- Maps relationships between users and content topics
- Helps Twitter understand if your tweet fits your follower interests
- Key strategy: Stay in your niche or clearly signal topic shifts

Tweepcred: User reputation/authority scoring
- Higher-credibility users get more distribution
- Your past engagement history affects current tweet reach
- Key strategy: Build reputation through consistent engagement

Engagement Signals Tracked

Twitter's Unified User Actions service tracks both explicit and implicit signals:

Explicit Signals (high weight):
- Likes (direct positive signal)
- Replies (indicates valuable content worth discussing)
- Retweets (strongest signal - users want to share it)
- Quote tweets (engaged discussion)

Implicit Signals (also weighted):
- Profile visits (curiosity about the author)
- Clicks/link clicks (content deemed useful enough to explore)
- Time spent (users reading/considering your tweet)
- Saves/bookmarks (plan to return later)

Negative Signals:
- Block/report (Twitter penalizes this heavily)
- Mute/unfollow (person doesn't want your content)
- Skip/scroll past quickly (low engagement)

The Feed Generation Process

Your tweet reaches users through this pipeline:

1. Candidate Retrieval - Multiple sources find candidate tweets:
2. Search Index (relevant keyword matches)
3. UTEG (timeline engagement graph - following relationships)

4. Tweet-mixer (trending/viral content)

5. Ranking - ML models rank candidates by predicted engagement:

6. Will THIS user engage with THIS tweet?
7. How quickly will engagement happen?

8. Will it spread to non-followers?

9. Filtering - Remove blocked content, apply preferences

10. Delivery - Show ranked feed to user

Optimization Strategies Based on Algorithm Insights

1. Maximize Real-graph (Follower Engagement)

Strategy: Make content your followers WILL engage with

• Know your audience: Reference topics they care about
• Ask questions: Direct questions get more replies than statements
• Create controversy (safely): Debate attracts engagement (but avoid blocks/reports)
• Tag related creators: Increases visibility through networks
• Post when followers are active: Better early engagement means better ranking

Example Optimization:
- ❌ "I think climate policy is important"
- ✅ "Hot take: Current climate policy ignores nuclear energy. Thoughts?" (triggers replies)

2. Leverage SimClusters (Community Resonance)

Strategy: Find and serve tight communities deeply interested in your topic

• Pick ONE clear topic: Don't confuse the algorithm with mixed messages
• Use community language: Reference shared memes, inside jokes, terminology
• Provide value to the niche: Be genuinely useful to that specific community
• Encourage community-to-community sharing: Quotes that spark discussion
• Build in your lane: Consistency helps algorithm understand your topic

Example Optimization:
- ❌ "I use many programming languages"
- ✅ "Rust's ownership system is the most underrated feature. Here's why..." (targets specific dev community)

3. Improve TwHIN Mapping (Content-User Fit)

Strategy: Make your content clearly relevant to your established identity

• Signal your expertise: Lead with domain knowledge
• Consistency matters: Stay in your lanes (or clearly announce a new direction)
• Use specific terminology: Helps algorithm categorize you correctly
• Reference your past wins: "Following up on my tweet about X..."
• Build topical authority: Multiple tweets on same topic strengthen the connection

Example Optimization:
- ❌ "I like lots of things" (vague, confuses algorithm)
- ✅ "My 3rd consecutive framework review as a full-stack engineer" (establishes authority)

4. Boost Tweepcred (Authority/Credibility)

Strategy: Build reputation through engagement consistency

• Reply to top creators: Interaction with high-credibility accounts boosts visibility
• Quote interesting tweets: Adds value and signals engagement
• Avoid engagement bait: Doesn't build real credibility
• Be consistent: Regular quality posting beats sporadic viral attempts
• Engage deeply: Quality replies and discussions matter more than volume

Example Optimization:
- ❌ "RETWEET IF..." (engagement bait, damages credibility over time)
- ✅ "Thoughtful critique of the approach in [linked tweet]" (builds authority)

5. Maximize Engagement Signals

Explicit Signal Triggers:

For Likes:
- Novel insights or memorable phrasing
- Validation of audience beliefs
- Useful/actionable information
- Strong opinions with supporting evidence

For Replies:
- Ask a direct question
- Create a debate
- Request opinions
- Share incomplete thoughts (invites completion)

For Retweets:
- Useful information people want to share
- Representational value (tweet speaks for them)
- Entertainment that entertains their followers
- Information advantage (breaking news first)

For Bookmarks/Saves:
- Tutorials or how-tos
- Data/statistics they'll reference later
- Inspiration or motivation
- Jokes/entertainment they'll want to see again

Example Optimization:
- ❌ "Check out this tool" (passive)
- ✅ "This tool saved me 5 hours this week. Here's how to set it up..." (actionable, retweet-worthy)

6. Prevent Negative Signals

Avoid:
- Inflammatory content likely to be reported
- Targeted harassment (gets algorithmic penalty)
- Misleading/false claims (damages credibility)
- Off-brand pivots (confuses the algorithm)
- Reply-guy syndrome (too many low-value replies)

How to Optimize Your Tweets

Step 1: Identify the Core Message

• What's the single most important thing this tweet communicates?
• Who should care about this?
• What action/engagement do you want?

Step 2: Map to Algorithm Strategy

• Which Real-graph follower segment will engage? (Followers who care about X)
• Which SimCluster community? (Niche interested in Y)
• How does this fit your TwHIN identity? (Your established expertise)
• Does this boost or hurt Tweepcred?

Step 3: Optimize for Signals

• Does it trigger replies? (Ask a question, create debate)
• Is it retweet-worthy? (Usefulness, entertainment, representational value)
• Will followers like it? (Novel, validating, actionable)
• Could it go viral? (Community resonance + network effects)

Step 4: Check Against Negatives

• Any blocks/reports risk?
• Any confusion about your identity?
• Any engagement bait that damages credibility?
• Any inflammatory language that hurts Tweepcred?

Example Optimizations

Example 1: Developer Tweet

Original:

"I fixed a bug today"

Algorithm Analysis:
- No clear audience - too generic
- No engagement signals - statements don't trigger replies
- No Real-graph trigger - followers won't engage strongly
- No SimCluster resonance - could apply to any developer

Optimized:

"Spent 2 hours debugging, turned out I was missing one semicolon. The best part? The linter didn't catch it.

What's your most embarrassing bug? Drop it in replies 👇"

Why It Works:
- SimCluster trigger: Specific developer community
- Real-graph trigger: Direct question invites replies
- Tweepcred: Relatable vulnerability builds connection
- Engagement: Likely replies (others share embarrassing bugs)

Example 2: Product Launch Tweet

Original:

"We launched a new feature today. Check it out."

Algorithm Analysis:
- Passive voice - doesn't indicate impact
- No specific benefit - followers don't know why to care
- No community resonance - generic
- Engagement bait risk if it feels like self-promotion

Optimized:

"Spent 6 months on the one feature our users asked for most: export to PDF.

10x improvement in report generation time. Already live.

What export format do you want next?"

Why It Works:
- Real-graph: Followers in your product space will engage
- Specificity: "PDF export" + "10x improvement" triggers bookmarks (useful info)
- Question: Ends with engagement trigger
- Authority: You spent 6 months (shows credibility)
- SimCluster: Product management/SaaS community resonates

Example 3: Opinion Tweet

Original:

"I think remote work is better than office work"

Algorithm Analysis:
- Vague opinion - doesn't invite engagement
- Could be debated either way - no clear position
- No Real-graph hooks - followers unclear if they should care
- Generic topic - dilutes your personal brand

Optimized:

"Hot take: remote work works great for async tasks but kills creative collaboration.

We're now hybrid: deep focus days remote, collab days in office.

What's your team's balance? Genuinely curious what works."

Why It Works:
- Clear position: Not absolutes, nuanced stance
- Debate trigger: "Hot take" signals discussion opportunity
- Question: Direct engagement request
- Real-graph: Followers in your industry will have opinions
- SimCluster: CTOs, team leads, engineering managers will relate
- Tweepcred: Nuanced thinking builds authority

Best Practices for Algorithm Optimization

1. Quality Over Virality: Consistent engagement from your community beats occasional viral moments
2. Community First: Deep resonance with 100 engaged followers beats shallow reach to 10,000
3. Authenticity Matters: The algorithm rewards genuine engagement, not manipulation
4. Timing Helps: Engage early when tweet is fresh (first hour critical)
5. Build Threads: Threaded tweets often get more engagement than single tweets
6. Follow Up: Reply to replies quickly - Twitter's algorithm favors active conversation
7. Avoid Spam: Engagement pods and bots hurt long-term credibility
8. Track Your Performance: Notice what YOUR audience engages with and iterate

Common Pitfalls to Avoid

• Generic statements: Doesn't trigger algorithm (too vague)
• Pure engagement bait: "Like if you agree" - hurts credibility long-term
• Unclear audience: Who should care? If unclear, algorithm won't push it far
• Off-brand pivots: Confuses algorithm about your identity
• Over-frequency: Spamming hurts engagement rate metrics
• Toxicity: Blocks/reports heavily penalize future reach
• No calls to action: Passive tweets underperform

When to Ask for Algorithm Optimization

Use this skill when:
- You've drafted a tweet and want to maximize reach
- A tweet underperformed and you want to understand why
- You're launching important content and want algorithm advantage
- You're building audience in a specific niche
- You want to become known for something specific
- You're debugging inconsistent engagement rates

Use Claude without this skill for:
- General writing and grammar fixes
- Tone adjustments not related to algorithm
- Off-Twitter content (LinkedIn, Medium, blogs, etc.)
- Personal conversations and casual tweets