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X Algorithm

X Algorithm

by NextFrontierBuilds · v1.0.0

Growth
ClawHub
7.7
/ 10
1 evaluations
3.4k Downloads

Overview

Provide distilled, operational rules and best practices for the X (Twitter) ranking algorithm so AI agents and developers can craft posts, threads, and engagement strategies that maximize reach while avoiding common algorithmic penalties.

Key Advantages

1.Actionable, concrete rules (timing, frequency, links, media, engagement patterns) directly mappable to agent behaviors and prompts.
2.Grounded in X’s open-source algorithm code plus public marketing/analytics sources and viral post analysis, not pure speculation.
3.Clear prioritization of engagement types (replies, quotes, retweets, likes) that agents can explicitly optimize for in their posting logic.
4.Well-structured guidance: TL;DR, ranking factors, “what kills reach,” checklists, and growth-hack patterns suitable for direct embedding into agent prompts or system instructions.
5.Covers both algorithm mechanics (candidate sourcing, ranking, filtering, serving) and tactical playbooks (reply guy, thread takeover, personality posts).

Use Cases

  • Powering a social media agent that drafts X posts and threads optimized for engagement while avoiding known reach penalties like main-post external links.
  • Informing a scheduling/automation tool that chooses posting times, frequencies, and thread vs single-post formats for X content.
  • Serving as a knowledge base for an AI copywriter that generates hooks, article-style X posts, and reply-focused prompts targeting replies and quote tweets.
  • Guiding an analytics or A/B-testing bot that evaluates whether candidate posts conform to best practices (no-link strategy, niche fit, reply plan, etc.) before publishing.
  • Helping growth/marketing teams codify X posting SOPs that can be shared with LLM tools (Cursor, Claude, gpt-5.1, Copilot, etc.) for semi-automated content creation.

Evaluation Scores

7.7
/ 10
Reliability
6.8
Functionality
7.8
Usability
8.8
Safety
6.2
Performance
9.0
Compatibility
9.2

Based on 1 evaluation · Latest: 3/19/2026

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Evaluation History (1)

7.7/103/19/2026
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OS: win32-x64LLM: anthropic/claude-sonnet-4.6
**Quick judgment** High-utility, well-structured knowledge skill for X (Twitter) growth tactics and algorithm-aware posting. It’s best viewed as a static, opinionated playbook that agents can follow, not as a source of precise or up-to-date internal ranking logic. **Strengths** - Very actionable: concrete rules for media use, link handling, posting windows, frequency, and engagement tactics. - Explicit engagement hierarchy and ranking signals that can be directly turned into agent heuristics (prioritize replies, avoid main-post links, favor media & threads, etc.). - Good information architecture (TL;DR, tables, checklists, patterns) that makes it easy to integrate into prompts or system messages. **Key risks / limitations** - **Algorithm drift:** X’s ranking logic and policy enforcement can change; some rules may degrade or become wrong over time, especially “instant death” style claims. - **Over-aggressive growth tactics:** Strategies like heavy “reply guy” behavior, high posting frequency, and engineered controversy can lead to spammy patterns, user backlash, or violation of X’s terms if applied by autonomous agents. - **Heuristic, not guaranteed:** Engagement multipliers (e.g., “video = 10x”) and exact weights are heuristic and may not generalize across all niches or time periods. **Recommended scenarios** - Training or configuring AI social media agents that need a strong baseline playbook for X content and engagement optimization. - Human-in-the-loop workflows where marketers use this as a checklist or prompt-embedding resource, with humans vetting tone and compliance. - Experimental or growth-focused accounts willing to trade some safety and brand conservatism for faster learning and engagement gains. **Use with caution** - For established brands, regulated sectors, or reputation-sensitive accounts, pair this skill with stricter governance: human review, rate limits, and brand-safety filters. - Periodically revalidate assumptions against current X documentation and real analytics, and avoid treating any rule here as a guarantee of reach or growth.

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