Analyze and rewrite tweets to maximize reach using Twitter's open-source recommendation algorithms.
Works with
Applies Real-graph, SimClusters, TwHIN, and Tweepcred models to identify optimization opportunities and explain algorithmic ranking decisions
Identifies engagement signals (likes, replies, retweets, bookmarks) and negative signals (blocks, reports, mutes) that affect tweet distribution
Rewrites tweets to trigger specific engagement patterns, target community resonance, and strengthen t
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontwitter-algorithm-optimizerExecute the skills CLI command in your project's root directory to begin installation:
Fetches twitter-algorithm-optimizer from composiohq/awesome-claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate twitter-algorithm-optimizer. Access via /twitter-algorithm-optimizer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Use this skill when you need to:
Twitter's recommendation system uses multiple interconnected models:
Real-graph: Predicts interaction likelihood between users
SimClusters: Community detection with sparse embeddings
TwHIN: Knowledge graph embeddings for users and posts
Tweepcred: User reputation/authority scoring
Twitter's Unified User Actions service tracks both explicit and implicit signals:
Explicit Signals (high weight):
Implicit Signals (also weighted):
Negative Signals:
Your tweet reaches users through this pipeline:
Candidate Retrieval - Multiple sources find candidate tweets:
Ranking - ML models rank candidates by predicted engagement:
Filtering - Remove blocked content, apply preferences
Delivery - Show ranked feed to user
Strategy: Make content your followers WILL engage with
Example Optimization:
Strategy: Find and serve tight communities deeply interested in your topic
Example Optimization:
Strategy: Make your content clearly relevant to your established identity
Example Optimization:
Strategy: Build reputation through engagement consistency
Example Optimization:
Explicit Signal Triggers:
For Likes:
For Replies:
For Retweets:
For Bookmarks/Saves:
Example Optimization:
Avoid:
Original:
"I fixed a bug today"
Algorithm Analysis:
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:
Original:
"We launched a new feature today. Check it out."
Algorithm Analysis:
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:
Original:
"I think remote work is better than office work"
Algorithm Analysis:
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:
Use this skill when:
Use Claude without this skill for:
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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twitter-algorithm-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
twitter-algorithm-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
twitter-algorithm-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: twitter-algorithm-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
twitter-algorithm-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added twitter-algorithm-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: twitter-algorithm-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: twitter-algorithm-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added twitter-algorithm-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
twitter-algorithm-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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