Agent-Skills.md

Agent Skills: X Algorithm Optimizer

Optimize X/Twitter content for algorithm engagement signals. Based on xai-org/x-algorithm's Grok transformer model that predicts 15 user-specific engagement signals. Activates for tweet optimization, thread strategy, X growth, or algorithm-aligned content.

UncategorizedID: shipshitdev/library/x-algorithm-optimizer

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shipshitdev
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skills/x-algorithm-optimizer/SKILL.md

Skill Metadata

Name
x-algorithm-optimizer
Description
Optimize X/Twitter content for algorithm engagement signals. Based on xai-org/x-algorithm's Grok transformer model that predicts 15 user-specific engagement signals. Activates for tweet optimization, thread strategy, X growth, or algorithm-aligned content.

X Algorithm Optimizer

Optimize content for X's algorithm based on actual engagement signal prediction (from xai-org/x-algorithm).

Core Insight: X's algorithm uses Grok-based transformers to predict 15 user-specific engagement signals. It optimizes for user relevance, not broad popularity.

When This Activates

  • User asks to optimize tweets for X algorithm
  • User wants to improve X/Twitter engagement
  • User asks about thread strategy
  • User mentions X growth or algorithm optimization
  • User wants to maximize reach or engagement on X

The 15 Engagement Signals

X's algorithm predicts these signals per-user:

Positive Signals (Maximize)

| Signal | Weight | Optimization Strategy | |--------|--------|----------------------| | Favorites | High | Relatable insights, contrarian takes, save-worthy content | | Replies | Very High | Questions, open loops, controversial hooks | | Reposts | Very High | Frameworks, data, templates, quotable insights | | Quotes | High | Hot takes people want to add to | | Shares | High | Actionable value, resources, tools | | Profile Clicks | High | Credibility signals, mysterious bio hooks | | Video Views | Medium | Hook in first 3s, text overlay, no slow intros | | Photo Expansions | Medium | Intriguing cropped previews, charts, screenshots | | Dwell Time | Very High | Long-form hooks, formatting, open loops | | Follows | Very High | Consistent niche value, credibility proof |

Negative Signals (Minimize)

| Signal | Trigger | Avoidance Strategy | |--------|---------|-------------------| | Not Interested | Irrelevant content | Stay on-niche, clear topic signals | | Blocks | Aggressive/spam behavior | No mass mentions, no DM spam | | Mutes | Posting frequency overload | Space out content, quality > quantity | | Reports | Policy violations | Clean content, no engagement bait |

Hook Formulas (Maximize Dwell Time)

Dwell time is critical. Stop the scroll with these patterns:

The Contrarian Hook

Most people think [common belief].

They're wrong.

Here's why:

The Credibility Hook

I've [impressive credential].

Here's what I learned:

The Data Hook

[Surprising statistic].

That's [comparison that makes it shocking].

The Story Hook

In [year], I was [relatable situation].

[Unexpected outcome] changed everything.

The Question Hook

Why do [successful people] always [behavior]?

I studied [number] of them. Here's the pattern:

The Scarcity Hook

[Number]% of people will never know this.

[Valuable insight]:

Reply Triggers (Maximize Replies)

Replies signal high engagement value to the algorithm.

Open-Ended Questions

  • "What would you add to this?"
  • "Unpopular opinion: [take]. Agree or disagree?"
  • "What's stopping you from [desired outcome]?"

Controversial Takes (Use Sparingly)

  • Challenge industry assumptions
  • Disagree with popular figures (respectfully)
  • Reframe common advice

Engagement Prompts

  • "Reply '[keyword]' if you want [resource]"
  • "Tag someone who needs to see this"
  • "What's your biggest challenge with [topic]?"

Open Loops

End tweets without full resolution:

  • "The real reason? I'll share in the thread below."
  • "But that's not the interesting part..."
  • "Here's what nobody talks about:"

Repost Patterns (Maximize Reposts)

Content people save and share:

Frameworks

The [Name] Framework for [Outcome]:

1. [Step with benefit]
2. [Step with benefit]
3. [Step with benefit]

Steal this.

Templates

Here's the exact [template/script/email] I used to [outcome]:

[Template]

Copy and use it.

Data/Stats

I analyzed [number] [things].

Here's what the data shows:

[Insight 1]
[Insight 2]
[Insight 3]

Bookmark this.

Resource Lists

[Number] [tools/resources/tips] that [benefit]:

1. [Name] - [1-line description]
2. [Name] - [1-line description]
...

Save for later.

Thread Architecture

Threads cascade engagement across tweets.

Structure

Tweet 1 (Hook): Stop the scroll, promise value
Tweet 2-6 (Body): Deliver value, one point per tweet
Tweet 7 (CTA): Follow, engage, or take action

Thread Rules

  1. Each tweet must stand alone (algorithm scores individually)
  2. Use "Thread" or number notation (1/7)
  3. End each tweet with curiosity for the next
  4. Put best content in tweets 2-3 (highest visibility)
  5. Include bookmarkable value (images, lists, frameworks)

Thread Hook Formula

I [credibility signal].

Here's [what I learned / my framework / the breakdown]:

(Thread)

Signal-Specific Optimization

Maximize Favorites

  • Relatable struggles + insights
  • "Finally someone said it" content
  • Save-worthy resources
  • Contrarian takes with evidence

Maximize Profile Clicks

  • Hint at more value in bio
  • Demonstrate niche expertise
  • Create curiosity about background
  • Strong credibility signals in content

Maximize Dwell Time

  • Long-form formatting (line breaks)
  • Numbered lists
  • Multiple scroll-stopping sections
  • Strategic use of images/video

Minimize Negative Signals

  • Stay consistent with niche
  • Don't post more than 3-5x/day
  • Avoid engagement bait ("Like if you agree")
  • No mass tagging or DM spam

Algorithm Mechanics

Author Diversity

The algorithm attenuates repeated creators in feeds. Implications:

  • Getting retweeted by diverse accounts > one mega account
  • Build relationships with different communities
  • Cross-pollination beats concentrated reach

User-Specific Relevance

Content is scored per-user, not globally. Implications:

  • Target your specific audience's interests
  • Build engagement patterns with your followers
  • Consistency matters more than virality

No Hand-Engineered Features

The model is pure ML prediction. Implications:

  • Gaming specific metrics doesn't work long-term
  • Focus on genuine engagement quality
  • Create content people actually want to engage with

Timing Guidance

| Audience Type | Best Times | Why | |--------------|------------|-----| | B2B/Tech | 8-10am, 12-1pm EST | Work hours, lunch breaks | | B2C/Lifestyle | 7-9am, 7-10pm EST | Before/after work | | Global | Varies | Test and measure |

Note: Timing matters less than content quality. A great tweet at 2am beats a mediocre tweet at peak time.

Quick Optimization Checklist

  • [ ] Hook stops the scroll in first line
  • [ ] Content delivers specific value
  • [ ] At least one engagement trigger (question, CTA)
  • [ ] Formatted for dwell time (line breaks, lists)
  • [ ] On-niche to avoid "not interested" signals
  • [ ] No engagement bait or spam patterns
  • [ ] Clear credibility signals where relevant

Integration

| Skill | When to Use | |-------|-------------| | content-creator | Generate tweet/thread content | | copywriter | Brand voice consistency | | prompt-engineer | Content generation prompts | | youtube-video-analyst | Apply hook patterns from video |

For detailed signal tactics and examples: references/engagement-signals.md