Linkedin Comment Drafter

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

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 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.
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. Call lib.publish(kind="comment", draft_text=, target_url=, post_urn=, platform_id=, reaction_type=). The wrapper handles Publora / manual / diy routing.

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

• T1 Missing-Piece (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

Global voice rules: see root SKILL.md §Voice rules. Additional skill-specific rules:

• 200-350 chars. Don't exceed.
• Always capitalize the author's name when addressing them by first name.
• 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/_activity-" > > 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sergebulaev
• Source: sergebulaev/linkedin-skills
• License: MIT
• Homepage: https://github.com/sergebulaev/linkedin-skills

Install and usage instructions live in the source repository linked above.