I open-sourced my LinkedIn prospect research tool as a Claude Code plugin
热度趋势
百分比基于当前可用热度信号，而非评论数或独立用户人数。
一位开发者将他的LinkedIn潜在客户研究工具开源，作为一个Claude Code插件，并采用MIT许可证。该工具通过Apify抓取LinkedIn帖子、个人资料和评论串，并存储到本地SQLite数据库中。它能对内容进行分类，从评论串中识别出正在讨论相关问题的人，并从完整的个人资料中发现不常见的共同点。此外，该工具还会记录外联活动并学习哪些策略能获得回复，同时强调数据本地存储且不包含遥测功能。
I do outreach for my startup. Started with Apollo and Clay doing mass automated messaging — conversion was bad. Switched to fewer, manually written, better targeted messages and reply rates went up a lot.
The bottleneck then became research: reading dozens of posts to work out what someone actually cares about. So I automated that part. Not the sending.
What it does - Scrapes LinkedIn posts, profiles and comment threads into local SQLite (via Apify) - Classifies what a person or company actually posts about - Mines a post's comment thread for people already describing your problem - Finds uncommon commonalities from full profiles — shared employers, schools, volunteer work - Logs what you sent and what came back, then learns which hooks get replies
18 MCP tools + 8 skills.
/plugin marketplace add spirosbax/insaight
/plugin install insaight@insaight
MIT, no telemetry, everything stays local: https://github.com/spirosbax/insaight