x-create

Create viral X (Twitter) posts including short tweets, threads, and replies. Use when user wants to write X content, create posts, or mentions "create tweet", "write thread", "x-create", "写推文", "创作推文". Supports 5 post styles with customizable templates, plus a mandatory humanize pass to reduce AI-sounding phrasing. First-time users go through onboarding to set up profile.

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Install skill "x-create" with this command: npx skills add kangarooking/x-skills/kangarooking-x-skills-x-create

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

TypeStyleUse WhenIntent Signals (路由线索)
高价值干货信息密度高,可收藏教程、工具推荐、方法论目标是收藏/转发;强调可执行清单、工具、步骤
犀利观点有态度有立场行业评论、反常识观点目标是讨论/对立;需要强立场、对比、反常识
热点评论快速反应新闻评论、事件点评目标是蹭热度/抢时效;围绕刚发生事件快速解读
故事洞察个人经历+洞察案例分析、经验复盘目标是共鸣/关注;用具体场景+转折+金句
技术解析深度技术原理讲解、源码分析目标是建立专业度;解释原理、机制、影响与建议

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}"}'

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