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Agent Skill · 开发

linkedin-thread-engagement(linkedin 线程参与度)

linkedin-thread-engagement 用于辅助安全审计、权限检查和凭据风险排查,适合在 OpenClaw 中需要复核安全边界、认证流程或敏感配置时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

linkedin-thread-engagement 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 10,501。

总安装 2,225周安装 90GitHub Stars 公开资料未说明下载 698核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:linkedin-thread-engagement(linkedin 线程参与度)
来源仓库:https://github.com/sergebulaev/linkedin-thread-engagement
安装命令:
openclaw skills install linkedin-thread-engagement
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install linkedin-thread-engagement

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

跟踪您在 LinkedIn 上的评论,以便作者在 72 小时内回复,标记高价值的参与窗口,并起草及时的后续行动,以最大限度地提高帖子动力。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install linkedin-thread-engagement;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

linkedin-thread-engagement 怎么安装?

在终端执行 openclaw skills install linkedin-thread-engagement。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

linkedin-thread-engagement 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

linkedin-thread-engagement 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

linkedin-thread-engagement 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

linkedin-thread-engagement 用的人多吗?

公开数据是累计安装 2,225 次,近一周安装 90 次,下载 698 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

linkedin-thread-engagement 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
linkedin-thread-engagement

linkedin 线程参与度 · 本页

2,225—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
linkedin-thread-engagement
description
Monitor LinkedIn threads where the user commented for author replies and inbound signals. Use when the user wants to track which of their comments earned personal replies from post authors (the highest-value engagement signal). Flags the 6-24h "Kevin Payne window" where author replies are most likely, drafts follow-up responses, and optionally routes to DM. Keywords: thread monitoring, author reply, inbound tracking, comment follow-up, engagement compound.

LinkedIn Thread Engagement

The engagement compounding layer. Tracks which of the user's comments earned author replies, drafts timely follow-ups, and flags the 6-24 hour window where thread momentum is highest.

When to use

  • Daily: "What threads need follow-up today?"
  • After posting a batch of comments: "Check back in 6 hours"
  • When an author replied personally (e.g., Kevin Payne → Serge): "Draft the response"

Input

  • User's LinkedIn profile URL (to pull their recent comments)
  • Optional: specific post URL to monitor

Output

Daily report

PostedAuthorPostCommentReply?StageAction
18h agoKevin PayneLawVu"moat moved to taste"✅ Kevin replied 14h agoWarm (6-24h window)Reply now
22h agoDharmesh ShahHubSpot"integration depth moat"NoColdSkip
3h agoFelix T.Rezolve"twin economies"NoWatchCheck in 3h

For each warm thread

  • Thread preview (last 3 turns)
  • Suggested response (drafted via linkedin-reply-handler)
  • Reaction target (the specific reply URN, not the post)
  • Priority (high / medium / low)

Weekly roll-up

  • Total comments posted
  • Author-reply rate (target: 15%+)
  • Conversion to DM (when thread closes warm)

Steps

  1. Fetch user's recent comments via HarvestAPI /linkedin/profile-comments.
  2. For each comment posted in last 72h: fetch the parent post's comment tree and look for:

- Replies to the user's comment - Whether the author posted any of those replies - Timestamps (time since user's comment, time since latest reply)

  1. Classify stage:

- Hot (<6h): author just replied — respond within 90 min for max thread momentum - Warm (6-24h): the Kevin Payne window — author replies most happen here - Cool (24-72h): still respondable but lower velocity - Dormant (>72h): don't reply in thread; consider DM

  1. Draft responses for warm threads using linkedin-reply-handler (which adapts to the active backend per lib.active_backend() — Publora auto-posts, manual mode returns copy-paste, DIY invokes custom poster).
  2. Flag suspicious patterns:

- Author replied but also deleted someone else's comment (author is actively moderating, tread carefully) - Commenter is in thread self-promoting (your reply shouldn't engage them)

  1. DM routing: if thread is dormant but the author engaged meaningfully, draft a DM that references the thread specifically.

Kevin Payne window

Named after the real 2026-04 data point: Kevin Payne (LawVu CEO) replied to Serge's comment 22h after the original post. This is the sweet spot.

  • 0-6h: 70% of author replies happen here if they're going to happen
  • 6-24h: ~25% of author replies, but these are higher-quality (author took time to think)
  • >24h: thread rarely produces new author engagement

Follow-up timing:

  • If author replied in 0-6h window: respond within 90 minutes
  • If author replied in 6-24h window: respond within 2 hours (they're still checking)
  • If author replied >24h: respond within 4 hours before thread goes cold

Inbound-quality signals

High-quality commenter = worth the follow-up:

  • Founder/operator title in profile
  • Company in user's ICP
  • Active posting history (not just reactions)
  • Mutual 2nd-degree connections >10
  • Prior thoughtful comments on user's posts

Low-quality = skip:

  • Generic praise with no specifics
  • Template language ("I'd love to hop on a quick call")
  • Profile is sales/agency with no operator history
  • Same comment across many creators' posts

Hard rules

  • Never reply to a reply later than 72h after the thread's last turn. Switch to DM.
  • Never chain 3+ replies under one comment (thread spam).
  • If the author deleted their reply, do not reply — they reconsidered.
  • Don't DM a warm thread before first replying publicly (skips a step).

Example

Input: monitor sbulaev profile, last 24h
Output: - 1 warm thread: Kevin Payne replied 14h ago on LawVu post. Current stage: Warm (8-24h). Suggested response ready. Action: post within 2 hours. - 8 cold threads (no author engagement). Skip. - 3 watching threads (<6h old, author may still reply). Check again in 3-6h.

Files

  • SKILL.md — this file
  • references/thread-timing.md — the timing matrix with examples

Related skills

  • linkedin-reply-handler — drafts the actual follow-up message
  • linkedin-comment-drafter — drafts the initial comment that starts threads

平台分布

OpenClaw

83.56%
按下载量换算583

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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