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linkedin-comment-drafterlinkedin 评论起草者

Agent Skill

linkedin-comment-drafter 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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。

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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-comment-drafter(linkedin 评论起草者)
来源仓库:https://github.com/sergebulaev/linkedin-comment-drafter
安装命令:
openclaw skills install linkedin-comment-drafter
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-comment-drafter

简介

使用经过验证的 2026 模板从帖子 URL 起草 1-3 个定制的、高参与度的 LinkedIn 评论选项,并在发布前等待用户批准。

SKILL.md

name
linkedin-comment-drafter
description
Draft a high-quality LinkedIn comment on any post from a URL. Use when the user gives a LinkedIn post URL and asks to comment on it. The skill parses the URL, reads the post context, drafts 1-3 comment variants in the user's voice using 2026 hook patterns (first-commenter, data-first, answer-the-closing-question, quotable-reframe), picks a reaction type, and waits for approval before posting via Publora. Keywords: linkedin comment, engage post, comment draft, first commenter, linkedin reply strategy.

LinkedIn Comment Drafter

Produce conversation-provoking comments on any LinkedIn post from a URL. The skill targets the patterns that actually got author replies in 2026 testing (Kevin Payne / Ivan Tsybaev patterns) and avoids the thesis-restatement patterns that die with zero engagement.

When to use

  • User pastes a LinkedIn post URL and says "comment on this", "draft me a comment", "engage with this post"
  • User wants to be among the first 3 commenters on a viral post
  • User wants to reply to a closing question the author asked

Input

A LinkedIn post URL in any of the standard shapes (see the top-level SKILL.md URL table).

Output

1-3 draft comment variants, each with:

  • 200-350 char body, 1-2 short paragraphs, no em dashes, no hashtags
  • Assigned reaction type: LIKE, PRAISE, EMPATHY, INTEREST, APPRECIATION, or ENTERTAINMENT
  • Pattern label (which of the 7 templates was used)
  • Estimated engagement fit based on what the author typically responds to

Then waits for user approval. On "post", calls Publora to react + comment.

Steps

  1. Parse the URL. Use lib.url_parser.parse_linkedin_url to get post_urn and, if present, the post's activity ID.
  2. Fetch the post body. If HarvestAPI is available via corporate-knowledge/personal/knowledge/tools/social_poster/src/harvest_client.py, pull the post text and top 3 existing comments (to avoid duplicate takes). If not, ask the user to paste the post text.
  3. Detect the author's closing question. If the post ends with a "?" line, the Answer-the-Closing-Question template usually wins.
  4. Draft comment variants. Pick 2-3 templates from references/comment-templates.md that fit the post's topic. Fill them with user-voice phrasing.
  5. Run the humanizer pass. Strip em dashes, AI vocab, uniform sentence rhythm. Add a specific number or named entity if missing.
  6. Present drafts for approval using lib.approval.render_approval_card. Include: target URL, each variant, reaction suggestion, a one-line "why this template fits".
  7. On approval — adapt to the active backend. Call lib.active_backend():

- publora (PUBLORA_API_KEY set) → react to the post with the chosen reaction type, pause 8-15s, then post via lib.PubloraClient.create_comment (top-level, no parent_comment). Return the comment URN. - manual (no backend configured — the default) → output the approved draft via lib.manual_mode_message(draft_text, target_url, kind="comment"). This gives the user a copy-paste block plus a one-time setup prompt for Publora (the preferred auto-post path). Do NOT attempt to post programmatically. - diy (LINKEDIN_SKILLS_CUSTOM_POSTER set) → invoke the user's configured custom poster command with the draft text + target URL as arguments.

Templates (see references/comment-templates.md for full list)

  • T1 Missing-Piece (Kevin Payne pattern, highest hit rate): [Name] the [their-thesis] argument misses one piece.. [what-moved]. when [their-condition], the real differentiator is [specific-skill], not [their-focus].
  • T2 Answer-the-Closing-Question: direct answer + one concrete example + why it matters
  • T3 Data-First: half the [population] I see now [behavior]. the [old-assumption] broke around [date]. [new-rule].
  • T4 Practitioner Observation: when X the system does Y, when X' it does Y'. that's when [outcome] kicks in.
  • T5 Counter-with-Concession: agree on point 1, push back on point 2 with one rooted reason
  • T6 Quotable-Reframe: one line under 12 words + expansion
  • T7 Ask-a-Sharper-Question: the harder version of this question is..

Hard rules

  • 200-350 chars. Don't exceed.
  • Always capitalize the author's name (e.g., "Dharmesh", not "dharmesh").
  • No em dashes, no hashtags, no emoji unless the post itself uses them.
  • No mention of the user's own product by name. Describe what they do instead.
  • Never paste generic praise ("Great post!", "This.", "100%"). The skill refuses.
  • Skip the comment if the post is sponsored, a generic listicle, or the author has already deleted it.

Example invocation

User: "Comment on this: https://www.linkedin.com/posts/dharmesh_activity-7448808898326654978-iW20" Skill: [parses URL, fetches post, detects closing question "Seen this in your market?", drafts 3 variants] Skill returns: T2 Answer-the-Closing-Question variant as primary pick, with T1 Missing-Piece as backup, reaction INTEREST, one-line rationale, and approval prompt.

Files in this skill

  • SKILL.md — this file
  • references/comment-templates.md — the 7 templates with fill-in slots and real examples
  • references/voice-rules.md — the specific voice rules from user feedback memories

Related skills

  • linkedin-reply-handler — if you're replying to a comment (not posting top-level)
  • linkedin-humanizer — for aggressive AI-tell scrubbing
  • linkedin-hook-extractor — if you want to use the author's own hook as the basis for your reply

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

需要对比不同来源的安装命令和来源信息时

能力概览

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

75.64%
按下载量换算573

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

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