x-boost
- Repo stars 12
- License MIT
- Author updated Live
- Author repo go-viral
- Domain
- Documentation
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @guzus · MIT
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: x-boost
description: Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, im…
category: documentation
runtime: no special runtime
---
# x-boost output preview
## PART A: Task fit
- Use case: Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, improving post engagement, or analyzing why posts underperform. Helps craft posts optimized for the X recommendation algorithm based on open-source algorithm analysis. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “How the Algorithm Scores Posts / Instructions / 1. Hook First” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, improving post engagement, or analyzing why posts underperform. Helps craft posts optimized for the X recommendation algorithm based on open-source algorithm analysis. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “How the Algorithm Scores Posts / Instructions / 1. Hook First” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “How the Algorithm Scores Posts / Instructions / 1. Hook First”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: x-boost
description: Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, im…
category: documentation
source: guzus/go-viral
---
# x-boost
## When to use
- Optimize X/Twitter posts for maximum reach using algorithm insights. Use when writing tweets, improving post engagemen…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “How the Algorithm Scores Posts / Instructions / 1. Hook First” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "x-boost" {
input -> user goal + target files + boundaries + acceptance criteria
context -> How the Algorithm Scores Posts / Instructions / 1. Hook First
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} X Post Optimizer
Helps craft posts optimized for the X recommendation algorithm based on open-source algorithm analysis.
How the Algorithm Scores Posts
The algorithm predicts 19 engagement actions and combines them:
Positive signals (boost reach):
- Likes, Replies, Retweets, Quotes
- Dwell time (time spent reading)
- Profile clicks, Follows from post
- Shares (DM, copy link)
- Video quality views, Photo expands
Negative signals (kill reach):
- "Not interested" clicks
- Blocks, Mutes, Reports
Instructions
When asked to optimize a post or write for X:
1. Hook First
- Lead with the most compelling point
- Stop the scroll in first 5 words
- Use pattern interrupts
2. Maximize Dwell Time
- Add depth that rewards reading
- Use line breaks for scanability
- Include images/videos that make people pause
3. Encourage Replies
- End with questions
- Make takes that invite discussion
- Leave threads open-ended
4. Avoid Author Penalty
The algorithm applies exponential decay to rapid posts from same author:
score = base_score × decay^(post_count)
Recommendation: Space posts 2-4 hours apart for maximum individual reach.
5. Leverage In-Network Advantage
Posts to followers rank higher than discovery posts. Build genuine following over chasing virality.
Quick Checklist
When reviewing a draft post, check:
- Hook in first line?
- Rewards reading (dwell time)?
- Invites replies?
- No spam/repetitive content?
- Authentic voice (not engagement bait)?
- Appropriate timing from last post?
What Doesn't Work
- Engagement pods (artificial patterns detected)
- Keyword stuffing (algorithm learns behavior, not keywords)
- Rapid-fire posting (author diversity penalty)
- Controversial content that triggers blocks/mutes
Example Optimization
Before:
Just launched my new product! Check it out at example.com
After:
I spent 6 months building something I wish existed 3 years ago.
The problem: [specific pain point]
The solution: [what you built]
Here's what surprised me most about the process:
[insight that invites discussion]
What's been your experience with [related topic]?
Why it's better:
- Hook creates curiosity (dwell time)
- Structure rewards reading
- Ends with question (replies)
- Authentic story (avoids mute/block signals)
Decide Fit First
Design Intent
How To Use It
Boundaries And Review