Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Tweet Composer
Score and optimize tweets based on X's real open-source ranking algorithm. Analyzes draft tweets against the actual ranking code — not generic tips. Use when...
skill openclawclawhub Free
Score and optimize tweets based on X's real open-source ranking algorithm. Analyzes draft tweets against the actual ranking code — not generic tips. Use when...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, assets/banner.svg, references/algorithm-rules.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- minilozio
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Tweet Composer
Score and optimize tweets using rules derived from X's open-source ranking algorithm.
How It Works
X's "For You" feed is ranked by a Grok-based transformer (Phoenix) that predicts 19 engagement actions for every candidate tweet. The final score is a weighted sum of these predictions. This skill encodes the structural rules from that pipeline into a scoring system. For the full algorithm breakdown, read references/algorithm-rules.md.
Scoring a Draft Tweet
When a user asks to score or optimize a tweet draft: Read references/algorithm-rules.md for the complete rules engine Analyze the draft against all rules Output the score card in this format: 🐦 Tweet Composer — Score: XX/100 [Category scores with ✅ ⚠️ ❌ indicators] 📊 Predicted Action Boost: ├─ P(reply): [assessment] ├─ P(favorite): [assessment] ├─ P(share): [assessment] ├─ P(dwell): [assessment] └─ P(not_interested): [assessment] 💡 Suggestions: → [actionable improvements] ✏️ Optimized version: "[rewritten tweet]"
Scoring Rubric (Quick Reference)
Score 0-100 based on weighted categories: CategoryWeightWhat to checkReply potential25Questions, opinions, CTAs that drive repliesMedia20Native image/video attached (not link previews)Shareability15Would someone DM this or copy the link?Dwell time15Length that makes people stop scrollingContent quality10Clear, original, not genericFormat10No links in body, no hashtags, good lengthNegative signals5Risk of not_interested/mute/block
Thread Optimization
When composing threads: First tweet = strongest hook (DedupConversationFilter keeps only the best per conversation) 3-6 tweets max (short threads > mega-threads) Each tweet self-contained (many see only the first) Media on tweet 1 or 2 for photo_expand boost CTA in last tweet
Quick Rules (No Reference File Needed)
Links: Always in reply, never in body (learned penalty from lower engagement) Hashtags: Zero. The model learns they reduce engagement Length: 100-200 chars sweet spot for single tweets Media: Native image/video = separate P(photo_expand) and P(video_quality_view) predictions Video: Must exceed minimum duration threshold for VQV weight to apply Timing: Post when your audience is active — engagement velocity in first 30 min is critical Frequency: AuthorDiversityScorer penalizes exponentially: 2nd post ~55% score, 3rd ~33%. Max 3-4 strong tweets/day Quote tweets: P(quote) has dedicated weight — QTs with added value outperform plain retweets Engagement bait: Questions/polls drive P(reply). "What would you add?" > "Like if you agree"
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Assets
- SKILL.md
- README.md
- references/algorithm-rules.md
- assets/banner.svg