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linkedin-reply-handlerlinkedin 回复处理程序

Agent Skill

linkedin-reply-handler 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

总安装

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下载量

783
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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-reply-handler(linkedin 回复处理程序)
来源仓库:https://github.com/sergebulaev/linkedin-reply-handler
安装命令:
openclaw skills install linkedin-reply-handler
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-reply-handler

简介

从给定的评论 URL 起草精确的 LinkedIn 评论回复,处理线程结构以在正确的顶级评论 URN 下发布。

SKILL.md

name
linkedin-reply-handler
description
Draft a reply to any existing LinkedIn comment from a URL. Use when the user wants to reply to a comment on someone else's post, reply to a reply on their own post, or follow up in a thread where the author just responded. The skill parses the commentUrn from the URL, figures out the correct parentComment target (LinkedIn flattens threads to 2 levels), drafts the reply in the user's voice, and waits for approval before posting via Publora. Keywords: linkedin reply, reply to comment, thread continuation, comment URL, parent comment URN.

LinkedIn Reply Handler

Drafts a reply to a specific LinkedIn comment. Correctly handles LinkedIn's 2-level thread flattening: if you're replying to a reply, the Publora API needs the TOP-level comment URN as parentComment, not the reply's URN.

When to use

  • User pastes a LinkedIn comment URL (contains ?commentUrn=...) and says "reply to this"
  • An author (e.g., Kevin Payne, Felix Tseitlin) replied to the user's comment and the user wants to continue the thread
  • User wants to re-engage a conversation that's gone dormant

Input

A LinkedIn URL containing commentUrn=urn:li:comment:(activity:POST,COMMENT_ID) — either the direct comment permalink or a feed URL with the query fragment.

Output

  • 1-2 reply drafts, 150-300 chars each
  • Reaction suggestion for the comment being replied to (always react before replying)
  • Thread context summary (who said what, when)
  • Approval card → on user "post", fires reaction + reply via Publora

Steps

  1. Parse the URL. lib.url_parser.parse_linkedin_url returns post_urn, comment_id, comment_urn.
  2. Determine thread structure. Fetch the post's comment thread (HarvestAPI if available) and locate the comment. Figure out whether it's:

- a top-level comment (parentComment = this comment's URN when replying) - a reply to a top-level comment (parentComment = the TOP comment's URN, not this reply's URN — LinkedIn flattens)

  1. Read the full context. Author post text, top-level comment text, any intermediate replies. Include the user's own prior comment if they're in the thread.
  2. Draft the reply. Follow the engagement templates in references/reply-templates.md. If the counterpart asked a question, answer it directly. If they pushed back, concede then sharpen.
  3. Humanizer pass. Strip em dashes, AI vocab, enforce varied sentence length.
  4. Approval card. Include thread preview (who said what in last 3 turns), the draft, reaction suggestion, and the parentComment URN we'll send.
  5. On approval — adapt to the active backend. Call lib.active_backend():

- publora (PUBLORA_API_KEY set) → react on the specific comment being replied to, pause 8-15s, then post reply with the correct top-level parentComment URN. - manual (no backend configured — the default) → output the approved reply via lib.manual_mode_message(draft_text, target_url, kind="reply"). Include the parent comment URL so the user knows exactly where to paste. Do NOT attempt to post. - diy (LINKEDIN_SKILLS_CUSTOM_POSTER set) → invoke the custom poster with draft, target URL, and parent-comment URN.

The flattening gotcha

LinkedIn only nests replies two levels deep. Visually the thread looks like:

Top comment by Alice (id: 111)
└─ Reply by Bob (id: 222)          ← parentComment: urn:li:comment:(activity:POST, 111)
   └─ Reply by Carol (id: 333)     ← parentComment: STILL urn:li:comment:(activity:POST, 111)

Carol's reply doesn't nest under Bob's — it's pinned at level 2 to the same top comment. If you pass urn:li:comment:(activity:POST, 222) as parentComment, the API returns 400 on some paths or silently misplaces the reply.

Rule in this skill: always use the TOP-level comment's URN as parentComment. If you're replying to a 2nd-level reply, we walk up the tree to find the top comment.

Templates (references/reply-templates.md)

  • R1 Answer-Their-Question — they asked, you answer plainly + one real detail
  • R2 Concede-Then-Sharpen — "you're right on X, and the piece I'd push on is Y"
  • R3 Extend-Their-Thesis — take their point one layer deeper with a new framing
  • R4 Share-Lived-Experience — "we hit this last quarter — here's what broke"
  • R5 Ask-Back — redirect with a sharper question when their position needs more context

Hard rules

  • 150-300 chars. Replies are tighter than top-level comments.
  • React to the comment you're replying to, not to the parent post.
  • Capitalize the counterpart's first name.
  • Never paste a canned "thanks!" — either respond with content or don't reply.
  • If the thread is older than 72 hours, consider a DM instead (use linkedin-thread-engagement).

Example

User: "Reply to this: https://www.linkedin.com/feed/update/urn:li:activity:7449018753880834048?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7449018753880834048%2C7449758545140453376%29" Skill: parses → post 7449018753880834048, comment 7449758545140453376. Fetches thread. Sees: Kevin Payne's post → Serge's comment ("moat moved to taste") → Kevin's reply ("How are you building that conviction muscle with your team?"). Drafts R1 Answer-Their-Question variant. Shows approval card. User: "post" Skill: react APPRECIATION on Kevin's reply → pause 12s → post reply with parentComment set to Serge's original comment URN (the TOP level, not Kevin's reply).

Files

  • SKILL.md — this file
  • references/reply-templates.md — 5 reply templates with examples
  • references/threading-rules.md — LinkedIn's 2-level flattening explained with edge cases

适合场景

01

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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补充不同宿主或平台的使用分布数据

能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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