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linkedin-comment-drafter

sergebulaev · Разработка

Draft a LinkedIn comment on someone else's post from its URL, or reshare (repost) it to your feed with optional commentary. Use when the user pastes a post URL and asks to comment, engage, be first commenter, or repost with their thoughts. Produces 1-3 variants in the user's voice, picks a reaction, and publishes via Publora on approval. Not for replying to existing comments (use linkedin-reply-handler).

Оригинален текст · няма потвърден превод на български
Платформи: Не са посочениНиво: Не е посоченоВ индекса: 22.09.2026 г.Обновен в GitHub: 18.09.2026 г.

Теми: agent-skill · agent-skills · ai-agents · ai-content · ai-marketing · anthropic · awesome-claude · claude-code · claude-skills · content-creation · content-engineering · linkedin · linkedin-automation · linkedin-engineering · llm-tools

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Последна проверка на записа
22.09.2026 г.

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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 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
  • User wants to reshare/repost a post to their own feed, with or without a one-line take ("repost this with my thoughts", "reshare this")

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, em dashes capped (about one per 100 words), 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

Voice profile first (all drafts). If ../../references/voice-profile.md has filled: yes, load it and match the user's voice fingerprint, hard rules, and CTA/link style throughout. If it is not filled, mention once that linkedin-humanizer --mode profile can learn their voice from a few posts, then proceed with the generic voice rules. If ../../references/story-bank.md has filled: yes, load it too and take concrete details (numbers, dates, named projects) from there instead of asking mid-draft. Never invent a figure that is not in it; if the bank has nothing that fits, ask the user or offer linkedin-interviewer.

  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 APIFY_TOKEN is set, call lib.ApifyClient.fetch_post(url) for the post body and fetch_post_comments(post_id=..., max_items=10) for the top existing comments (so your draft doesn't duplicate an existing take). Both actors are no-cookies and cost roughly $0.001 + $0.005 per call on the Apify free tier. If APIFY_TOKEN is not set, ask the user to paste the post text and (optionally) top comments.

Целият SKILL.md в GitHub