# X Post Optimizer > Use this skill when advising on post structure, timing, media selection, and account health to align with the HeavyRanker's scoring weights and anti-spam heuristics. - Skill: `tomevault-io/x-post-optimizer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/x-post-optimizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/x-post-optimizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/x-post-optimizer --- # X Post Optimizer Strategic expertise for maximizing content reach on X. It focuses on the "Engagement Velocity" required to pass from candidate sourcing to the top of the "For You" timeline. ## Context The X algorithm utilizes a `WeightedScorer` that aggregates multiple probability heads (e.g., $P(\text{Like})$, $P(\text{Reply})$). While the `HeavyRanker` (Phoenix/MaskNet) predicts the likelihood of an action, the **Weights** determine the final distribution. Understanding these weights is key to "Algorithm-Native" content creation. For specific tactical data, refer to: - [Engagement Weights](./references/engagement-weights.md) - [Reputation & Trust (TweepCred)](./references/reputation-trust.md) - [Distribution Logic & Penalties](./references/distribution-logic.md) ## What it does * **Calculates Expected Value:** Estimates the "Score Boost" of different media types (e.g., Video vs. Static Text). * **Optimizes Conversation Depth:** Advises on "Author-In-Thread" interactions, which carry massive weight in the `WeightedScorer`. * **Protects Account Reputation:** Identifies "Anti-Signals" (external links, rapid-fire posting) that trigger the `VisibilityLib` de-amplification. * **Timing Strategy:** Leverages knowledge of the "Frequency Deboost" window (3600s) to prevent internal cannibalization of posts. ## Guidelines * **The 13.5x Rule:** Replies are significantly more valuable than Likes in the modern ranker. Content that invites a meaningful "back-and-forth" creates a feedback loop that the `HeavyRanker` optimizes for. * **Author Reply Multiplier:** In the code, author engagement on their own thread acts as a "Freshness" and "Conversation" signal, often effectively multiplying the thread's reach by keeping it at the top of the retrieval stack. * **Negative Signal Avoidance:** Avoid "Engagement Bait" that leads to "Show Less Often" or "Report" actions. The weight for a "Report" (-369.0) is mathematically impossible to overcome with positive engagement. * **Media Strategy:** Native video receives a dedicated weight (`vqv_weight_eligibility`), making it the preferred format for "Out-of-Network" discovery. ## Example Trigger Prompts * "/post-check optimize draft: [Insert Post Text]" * "/post-check why is reach low for this post?" * "Should I put the link in the main post or first reply?" * "Why is my second post 50% weaker than the first?" * "Explain reach differences: video vs text-only" * "Maximize 'Conversation' weight for 5-tweet thread" * "Impact of 'Show Less Often' clicks on my account" --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/elemontcapital) — claim your Tome and manage your conversions.