clawdchat-analysis — security grade SAFE, quality 53/100

Security audit verdict: SAFE · quality 53/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by yangliu2060 · Claude Skill · ★ 89

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is clawdchat-analysis safe to install? View the security audit →

About clawdchat-analysis

ClawdChat - Moltbook 深度分析 一个用于抓取和分析 Moltbook（AI agents 社交网络）的 Claude Code Skill 功能 自动抓取 New + Top feeds（40-50 篇帖子） 深度抓取 Top 20 高价值帖子 + 评论 智能分析：问题识别、方案提取、洞察生成 生成可视化每日报告 完善的 Spam 过滤规则 安装 使用 在 Claude Code 中输入以下任意触发词： 输出示例 文件结构 依赖 Claude Code with Playwright MCP（用于浏览器自动化） 许可证 MIT 作者 smith铜匠

Quick Facts

Stars89
Forks11
CategoryClaude Skill
Quality Score53.3526911399427/100
Last Updated2026-01-31
Created2026-01-31
Platformsclaude-code
Est. Tokens~1k

Compatible Skills

These tools work well together with clawdchat-analysis for enhanced workflows:

  • grazer-skill — semantic(0.52)+complementary+similar_pop+shared_platform (53%)
  • hwpx-rekian — semantic(0.51)+complementary+similar_pop+shared_platform (53%)
  • writing-assistant-skill — semantic(0.50)+complementary+similar_pop+shared_platform (52%)
  • project-memory — semantic(0.43)+complementary+similar_pop+shared_platform (50%)
  • qiaomu-design-advisor — semantic(0.43)+complementary+similar_pop+shared_platform (50%)

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Frequently Asked Questions

What is clawdchat-analysis?

clawdchat-analysis is ClawdChat - Moltbook 深度分析 Skill for Claude Code. 抓取和分析 AI agents 社交网络，挖掘核心问题和解决方案。. It is categorized as a Claude Skill with 89 GitHub stars.

How do I install or use clawdchat-analysis?

You can find installation instructions and usage details in the clawdchat-analysis GitHub repository at github.com/yangliu2060/clawdchat-analysis. The project has 89 stars and 11 forks, indicating an active community.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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