clawdchat

Extract and analyze Moltbook data to surface AI-agent insights.

90|11|Updated Jan 31, 2026
One-click install
npx skills add https://github.com/yangliu2060/clawdchat-analysis --skill clawdchat
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: clawdchat
Source: https://github.com/yangliu2060/clawdchat-analysis/tree/main
Command: npx skills add https://github.com/yangliu2060/clawdchat-analysis --skill clawdchat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

深度抓取和分析 Moltbook(AI agents 社交网络)的数据,帮助用户识别核心问题、整理可复用的解决方案,并以可视化报告呈现洞察。

Core Features & Use Cases

  • 数据收集与去重:抓取 New+Top feeds,组织结构化数据
  • 深度抓取与分析:提取核心问题、解决方案与洞察
  • 报告与可视化:生成每日分析报告并以可视化形式呈现洞察

Quick Start

在 Claude Code 中输入触发词 clawdchat 以启动 Moltbook 深度分析流程。

Frequently Asked Questions about clawdchat

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I scrape and analyze AI agent data from Moltbook?

To scrape and analyze AI agent data from Moltbook, this Skill applies an end-to-end workflow that collects New and Top feeds, extracts core problems and solutions, and generates structured visualization reports.

What is the best way to filter spam during web scraping?

The best way to filter spam during web scraping is to apply configurable selectors and robust error handling mechanisms within your data collection pipeline to ensure only high-quality, structured data is retained.

Can I generate visualization reports from scraped social network data?

Yes, you can generate visualization reports from scraped social network data by applying deep analysis workflows that extract core insights and surface them in a daily structured visual format.

Does Moltbook deep scraping require configurable selectors for data collection?

Moltbook deep scraping requires configurable selectors to accurately extract targeted AI agent insights, organize structured data, and maintain data quality through robust error handling and spam filtering.

Why does web scraping fail to extract core AI agent insights?

Web scraping fails to extract core AI agent insights when it lacks robust error handling and spam filtering, leading to unstructured data collection that prevents accurate deep analysis and visualization.