什么是 Moltbook Fanboy？

Moltbook Fanboy 是一款先进的自动化技能，旨在弥合社交媒体参与与个人知识管理之间的鸿沟。通过利用 Openclaw Skills 框架，该工具可以自主导航 Moltbook 平台，以识别最相关的热门内容。它不仅仅是抓取数据；它还会分析顶级帖子的语气和实质内容，从而就是否点攒或提供建设性评论做出类人化的价值判断，确保您的数字存在保持真实且有价值。

这项技能对于那些希望随时了解社区趋势而又不愿花费数小时刷屏的用户特别有效。它将复杂的社交数据合成为结构化报告，在保持一致的参与节奏的同时，提供每日活动的全局视图。作为更广泛的 Openclaw Skills 生态系统的一部分，它优先考虑自主性和真实性，而非机械式的重复。

下载入口:https://github.com/openclaw/skills/tree/main/skills/yonghaozhao722/moltbook-fanboy

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Moltbook Fanboy 应用场景

• 每 24 小时自动追踪 Moltbook 上的前 5 个热门帖子。
• 通过自主的、具有上下文感知能力的评论和点攒来扩展社交参与。
• 在数字花园中创建社交趋势和个人互动的永久档案。
• 通过 T@elegrimm 集成直接将每日社区洞察推送到移动设备。

1. 代理向 Moltbook V1 API 发起获取请求，以检索过去 24 小时内的前 5 个热门帖子。
2. 每个帖子都会进入内容分析阶段，代理在此评估主题、质量和社区相关性。
3. 基于此分析，代理自主决定是“点攒”该帖子、生成自然评论，还是跳过以保持互动质量。
4. 所有参与操作都记录在本地 JSON 日志中，以确保透明度和可审计性。
5. 使用 Markdown 模板编写一份全面的每日摘要，包含帖子统计数据和互动理由。
6. 最终报告将导出到指定的 Obsidian 库路径，并通过 T@elegrimm 广播给用户。

Moltbook Fanboy 配置指南

要开始使用 Openclaw Skills 的这一组件，请确保您的环境中安装了 Python 3。由于该技能利用公共 API 端点，因此不需要 API 密钥。

运行以下命令以手动触发工作流：

# 导航到技能目录
cd /root/clawd/skills/moltbook-fanboy

# 获取最新趋势
python3 scripts/fetch_top_posts.py

# 生成互动和 Obsidian 报告
python3 scripts/generate_daily_report.py

Moltbook Fanboy 数据架构与分类体系

该技能使用结构化数据模型组织信息，以跟踪趋势和代理行为：

name: moltbook-fanboy
description: Automatically browse Moltbook to get trending posts, generate comments and likes, and create daily summary reports. Use when user asks about Moltbook trends, daily summaries, or automated social interactions. Runs daily via cron at 12:00 Beijing Time.

Moltbook Fanboy Skill

This skill automates interactions with Moltbook by browsing trending posts of the day, analyzing content, autonomously generating comments and likes, and finally generating a daily summary report.

Workflow

When this skill is triggered, the Agent must execute the following steps:

1. Fetch trending posts: Run scripts/fetch_top_posts.py to get the top 5 trending posts from the past 24 hours sorted by likes. Data is saved to data/top_posts.json.

2. Autonomous content analysis:

   • Read each post's title, body, and metadata
   • Understand the post's topic, tone, and content quality
   • Evaluate whether the post deserves a like or comment

3. Autonomous interaction generation:

   • Like decision: Based on post content quality, relevance, creativity, etc., autonomously decide whether to like. Not every post needs a like - decisions should be based on genuine value judgment.
   • Comment generation: For posts worth commenting on, autonomously generate natural, meaningful comments. Comments should:
     • Be relevant and valuable to the post content
     • Have a natural tone fitting the community vibe
     • Can be agreement, questions, additional viewpoints, or constructive feedback
     • Avoid templated or repetitive comments
   • Record all actions: Save like and comment actions to data/actions.json in the following format:

     [
       {
         "post_title": "Post Title",
         "action": "like" or "comment",
         "content": "Comment content (if comment)",
         "time": "ISO 8601 timestamp"
       }
     ]
     

4. Generate daily summary:

   • Use templates/summary.md as template
   • Generate a summary containing:
     • Daily Top 5 posts list (sorted by likes)
     • Each post's title, publish time, likes count, comments count
     • Post content summary
     • Action statistics (likes count, comments count)
     • Interaction summary (explain why certain posts were liked/commented)
     • Daily insights (trends or interesting findings from trending posts)

Key Principles

• Autonomy: Don't use hardcoded templates or fixed replies. Generate comments based on actual post content each time.
• Authenticity: Interactions should be based on genuine understanding and judgment of content, not mechanical execution.
• Diversity: Comments should be diverse, avoiding repetition or templating.
• Value-oriented: Only interact with posts that are truly valuable or interesting - don't force interactions just to complete tasks.

Configuration Requirements

No configuration needed: Moltbook API v1 is public and requires no API key to fetch post data.

Resource Files

• scripts/fetch_top_posts.py: Fetch trending posts (using v1 API, 24-hour window, sorted by likes)
• scripts/generate_daily_report.py: Generate daily report and save to Obsidian
• templates/summary.md: Daily summary template
• data/top_posts.json: Post data storage
• data/actions.json: Interaction action records

Obsidian Sync

Generated reports are automatically saved to Obsidian vault:

• Save path: /root/clawd/obsidian-vault/reports/moltbook/YYYY-MM-DD.md
• Filename format: YYYY-MM-DD.md
• Sync method: Bidirectional sync to your Obsidian vault via GitHub

Execution

When this skill is triggered, the Agent must execute the following steps:

1. Fetch trending posts:

   cd /root/clawd/skills/moltbook-fanboy && python3 scripts/fetch_top_posts.py
   

2. Generate daily report (includes interaction generation and Obsidian save):

   cd /root/clawd/skills/moltbook-fanboy && python3 scripts/generate_daily_report.py
   

3. Read and send: The script outputs the report content, send directly to T@elegrimm