# Twitter Algorithm Optimizer

Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm...

- Repository: https://github.com/dp-archive/archive (1107 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-03-04
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add dp-archive/archive --skill twitter-algorithm-optimizer -g -a claude-code -y`
Codex: `npx skills add dp-archive/archive --skill twitter-algorithm-optimizer -g -a codex -y`
Cursor: `npx skills add dp-archive/archive --skill twitter-algorithm-optimizer -g -a cursor -y`
Gemini CLI: `npx skills add dp-archive/archive --skill twitter-algorithm-optimizer -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/dp-archive/archive/blob/HEAD/seed_skills/twitter-algorithm-optimizer/SKILL.md