Imported from kangarooking/x-skills (x-create/SKILL.md). Install upstream with npx skills add kangarooking/x-skills --skill x-create. Copyright stays with the author.

X Create

Create viral X posts (short tweets, threads, replies) based on user's persona and post patterns.

First-Time Setup

Check user profile before creating content:

1. Read references/user-profile.md
2. If initialized: false or file doesn't exist → Run onboarding
3. If initialized: true → Proceed to content creation

Onboarding Questions

Ask user these questions using AskUserQuestion tool:

1. 账号定位（领域）: 你的X账号主要分享什么内容？

   • Options: AI/科技, 创业/商业, 个人成长, 投资理财, Other

2. 目标受众: 你的目标读者是谁？

   • Options: 中文用户, 英文用户, 双语用户

3. 人设风格: 你希望塑造什么样的人设？

   • Options: 专业严肃, 轻松幽默, 犀利观点, 温暖亲和, Other

After collecting answers, update references/user-profile.md with initialized: true.

Post Types

5 Categories

Output Formats

1. 短推文 (≤280 characters) - Single tweet
2. Thread (多条串联) - 3-10 tweets connected
3. 评论回复 - For replying to trending posts

Creation Workflow

Step 1: Load Context

1. Read references/user-profile.md → Get persona, style
2. (Optional) Read state from ~/.claude/skills/x-create/state/
   - liked_topics.json (positive samples)
   - rejected_topics.json (negative samples)
   - events.jsonl (optional)
3. Check assets/templates/{type}/ → Look for user reference posts
4. If no references → Use default patterns from references/post-patterns.md

Step 2: Intent-based Routing

Determine intent first, then choose style and format:

1. Intent → Style (5 categories)

   • 收藏/转发导向 → 高价值干货
   • 讨论/对立导向 → 犀利观点
   • 时效/热点导向 → 热点评论
   • 共鸣/关注导向 → 故事洞察
   • 专业/技术导向 → 技术解析

2. Style → Output format

   • Short tweet: Single insight, quick take, one-liner
   • Thread: Multi-point analysis, step-by-step, detailed breakdown
   • Reply: Designed to respond to specific post/topic

If user explicitly provides --type, follow it. Otherwise route automatically.

Step 3: Apply Pattern

Read references/post-patterns.md for the specific post type pattern.

Step 4: Generate Content (A/B Variants)

Create two variants by default:

• Variant A: More direct, stronger hook, higher contrast
• Variant B: More structured, more evidence, slightly more neutral

Follow:

1. User's persona style
2. Selected post style pattern
3. Reference examples (if available)

Step 4.5: Humanize Pass（去 AI 味，默认必做）

For each variant, rewrite the text to sound like a real person on X while keeping meaning and claims unchanged:

• Delete filler + chatbot politeness: avoid "当然/希望这对你有帮助/让我们来深入探讨"
• Remove grand/marketing tone: avoid "标志着/至关重要/不断演变的格局/彰显/赋能/令人叹为观止"
• No vague attribution: avoid "专家认为/行业报告显示" unless you provide a specific source; otherwise rewrite as "我观察到/我的判断是..."
• Reduce connective phrases: avoid overusing "此外/然而/因此"; prefer simple sentences and line breaks
• Break formula: do not force "三段式"; 2 points is fine; mix short + long sentences
• Avoid dash spam: do not stack "——"
• Prefer concrete details over empty conclusions; if you are unsure, say it plainly and briefly

Thread constraints:

• Each tweet must be <= 280 characters
• Do not make every tweet identical in structure; allow 1-2 short "pause" lines

Step 5: Critic (Self-evaluation) + Rewrite Once

Score the humanized Variant A/B as the target reader (0-10):

• Hook strength
• Information density / value
• Clarity and readability
• Credibility (no exaggeration / no made-up facts)
• Persona fit
• Action likelihood: like / repost / bookmark / reply
• "AI 味" control: no empty grand statements, no templated endings, no vague authority

Rules:

• If both Variant A and B score < 7, rewrite once (produce A2/B2), then run the same humanize pass again, and re-score.
• Select the best variant as final output, but still show both drafts.

Output Format

# 推文创作

## 选题
{topic}

## 推文类型
{short_tweet/thread/reply}

## 风格
{post_style}

---

## Drafts

### Variant A

{For short tweet: single tweet content}

{For thread:}
### 1/N
{first tweet}

### 2/N
{second tweet}

...

### N/N
{final tweet with call to action}

**Critic score (0-10)**: {critic_score_a}

### Variant B

{For short tweet: single tweet content}

{For thread:}
### 1/N
{first tweet}

### 2/N
{second tweet}

...

### N/N
{final tweet with call to action}

**Critic score (0-10)**: {critic_score_b}

---

## Selected

Selected variant: {A|B|A2|B2}
Reason: {one-sentence reason}

---

## 发布建议
- 最佳发布时间: {suggestion}
- 配图建议: {image suggestion if applicable}
- 预期互动: {engagement prediction}

下一步：运行 /x-publish 发布到草稿箱

Append machine-readable blocks for hooks/state ingestion:

CREATE_JSON
{
  "schema_version": "x_skills.create.v1",
  "topic": "{topic}",
  "post_type": "short|thread|reply",
  "post_style": "high-value|sharp-opinion|trending-comment|story-insight|tech-analysis",
  "variants": [
    {"id":"A","critic_score_0_10":0,"text":"..."},
    {"id":"B","critic_score_0_10":0,"text":"..."}
  ],
  "selected": "A|B|A2|B2",
  "rewrite_once": true
}

HOOKS_JSON
{
  "schema_version": "x_skills.hooks.v1",
  "topic": "{topic}",
  "hooks": [
    {"text":"...","source":"variant.A","tags":["数字|反常识|痛点|悬念|类比"],"score_0_10":0}
  ]
}

Template Priority

1. User templates first: Check assets/templates/{type}/
2. Default patterns: Use references/post-patterns.md

Example:

Creating 高价值干货 post:
1. Check assets/templates/high-value/
2. If files exist → Learn style from examples
3. If empty → Use default pattern from post-patterns.md

Resources

references/user-profile.md

User customization info (shared across all x-skills)

references/post-patterns.md

Default viral post patterns for 5 categories

assets/templates/

User-provided reference posts organized by type:

• high-value/ - 高价值干货类参考
• sharp-opinion/ - 犀利观点类参考
• trending-comment/ - 热点评论类参考
• story-insight/ - 故事洞察类参考
• tech-analysis/ - 技术解析类参考

Example

User: /x-create Claude 4.5 Opus发布 --type thread

1. Read user-profile.md → persona: 专业严肃、犀利观点
2. Check assets/templates/tech-analysis/ → empty
3. Read post-patterns.md → Get tech-analysis pattern
4. Generate thread:

### 1/5
Claude 4.5 Opus 上线了。我先说结论：它更像“慢一点，但更稳”的那类模型。

我用 3 个小任务试了下，写个线程记录👇

### 2/5
我最直观的感受不是“更聪明”，而是更会停下来检查自己。

同一个问题，它更少给“听起来对”的答案。

### 3/5
三个场景（都不算大项目）：
1) 重构一个旧模块：更愿意先问清边界，再动手改
2) 复杂推理题：会把关键假设写出来（这点很救命）
3) 长文档梳理：更少漏掉前后矛盾的地方

### 4/5
代价也很现实：
- 反应慢一点
- 成本可能更高（看你用的套餐/调用方式）
- 你得给它更明确的上下文

### 5/5
如果你做的是“错一次就很麻烦”的任务（代码、决策、长文整理），值得试。

只是日常闲聊，感知没那么强。你们试过了吗？

Integration

After creation, suggest:

推文创作完成！

- 类型: {thread/short/reply}
- 字数: {word_count}
- 预计阅读: {read_time}

下一步：运行 /x-publish 发布到X草稿箱

（反馈闭环，可选）
- 采纳并进入正样本：
  python ~/.claude/skills/x-create/scripts/x_state.py like --topic-json '{"title":"{topic}","selected":"{A|B}","critic_score":8}'
- 否决并进入负样本：
  python ~/.claude/skills/x-create/scripts/x_state.py reject --topic-json '{"title":"{topic}","reason":"low_value"}'
- 写入事件（hooks 自动收集也可用）：
  python ~/.claude/skills/x-create/scripts/x_state.py event --event create.generated --payload-json '{"topic":"{topic}","variants":["A","B"],"selected":"{A|B}"}'