/linkedin-comment-drafter

name: linkedin-comment-drafter
description: "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)."

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. 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.** Scrub 2026 AI vocab by paragraph density, cap em dashes (about one per 100 words, never swap one for a period), fix only machine-flat rhythm without manufacturing variance, and add an odd-precision number with a named referent if missing. Canonical rules: `linkedin-humanizer` V3. 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=<approved>, target_url=<post_url>, post_urn=<urn>, platform_id=<id>, reaction_type=<chosen>)`. The wrapper handles Publora / manual / diy routing.

Reshare mode (repost with your thoughts)

Same input as commenting (a post URL), but instead of commenting on the post you reshare it to the user's own feed, optionally with a short take above it. Use this when the ask is "repost", "reshare", or "share this with my network".

1. **Fetch the post** the same way (`lib.fetch_post(url)`), and check it is reshareable: the Apify payload exposes `canShare` and the `shareUrn` (`urn:li:share:*` / `urn:li:ugcPost:*`). If `canShare` is `False`, tell the user the author disabled resharing and stop. 2. **Draft the commentary** (optional). Keep it to one or two sentences in the user's voice: a genuine take, endorsement, or the reason this is worth a colleague's time. Run the same humanizer pass (em dashes capped, no AI vocab). A plain reshare with no commentary is also valid; skip the draft if the user just wants to amplify. 3. **Present for approval** with the original post URL and the drafted commentary (or "plain reshare, no commentary"). 4. **On approval.** Call `lib.repost(post_url, commentary=<approved or None>)`. The wrapper resolves the correct `shareUrn` from Apify (do not hand-convert an `activity` id, the share id can differ), refuses posts with resharing off, and routes Publora / manual / diy. Manual tier returns copy-paste steps ("Repost with your thoughts"). The new reshare URN is `result["reshare"]["id"]`.

Commentary cap is 3000 chars (LinkedIn), but a tight one or two sentences outperforms a wall of text. This is the tool `linkedin-employee-advocacy` uses to reshare brand and colleague posts.

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 doe