/linkedin-reply-handler

name: linkedin-reply-handler
description: "Draft a reply to one LinkedIn comment from its URL, or sweep a whole thread from just the post URL and draft a reply to every comment worth answering, in one batch. Use for replying to a comment, following an author reply, or clearing all comments on a post. Resolves the correct parentComment (LinkedIn flattens threads to 2 levels), filters low-value comments before a sweep, and posts via Publora on approval. Not for top-level comments (use linkedin-comment-drafter)."

LinkedIn Reply Handler

Drafts a reply to a specific LinkedIn comment, or sweeps an entire comment thread (every top-level comment and its replies) from just the post URL and drafts a reply to each one worth answering. Both modes correctly handle 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

**Single comment:**

• User pastes a LinkedIn comment URL (contains `?commentUrn=...`) and says "reply to this"
• An author 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

**Whole thread (just a post URL, no comment URLs):**

• User pastes a post URL and says "reply to all the comments", "clear my inbox on this post", "draft replies for everyone who commented", "sweep the comments on this post"
• User wants to catch up on a post that has accumulated comments over several days

Not for:

• Commenting on someone else's post (not replying to comments on the user's own post) → `linkedin-comment-drafter`
• Reading engagement without drafting anything → `linkedin-engager-analytics` or `linkedin-thread-monitor`

Input

Either shape works:

• 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. Triggers single-comment mode.
• Just a LinkedIn post URL, in any of the standard shapes (see root `SKILL.md` URL table) — no comment URLs needed. Triggers whole-thread mode.

Output

**Single comment:**

• 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

**Whole thread:**

• A filtered roster: how many comments were fetched, how many were filtered out and why, how many drafts follow
• One reply draft per comment worth replying to (150-300 chars each), each tagged with its target comment, the correct `parentComment` URN, and a reaction suggestion
• A single batch approval card covering every draft
• On approval, posts all of them (reaction + reply, per comment)

Steps — single comment

**Voice profile first (all drafts, both modes).** 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.** `lib.url_parser.parse_linkedin_url` returns `post_urn`, `comment_id`, `comment_urn`. 2. **Determine thread structure.** If `APIFY_TOKEN` is set, call `lib.ApifyClient.fetch_post_comments(post_id=post_urn, max_items=50)` and locate the comment by `comment_id`. Otherwise ask the user to paste the relevant slice of the thread. **With the Apify MCP connector available instead of a token**, read the same comments through its tool and drop the run-summary row before using them, per `../../references/mcp-read-layer.md`; that is the only read path on claude.ai. Figure out whether the target is:

• 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)

3. **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. 4. **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. 5. **Humanizer pass.** Scrub 2026 AI vocab by density, cap em dashes (about one per 100 words), fix only machine-flat rhythm and never manufacture sentence-length variance. Canonical rules: `linkedin-humanizer` V3. 6. **Approval card.** Include thread preview (who said what in last 3 turns), the draft, reaction suggestion, and the parentComment URN we'll send. 7. **On approval.** Call `lib.publish(kind="reply", draft_text=<approved>, target_url=<comment_url>, post_urn=<urn>, platform_id=<id>, parent_comment=<top_level_comment_urn>, reaction_type=<chosen>)`. The wrapper handles Publora / manual / diy routing.

Steps — whole thread

Same voice-profile-first rule applies. Then:

1. **Parse the post URL.** `lib.url_parser.parse_linkedin_url` to get `post_urn`. If the URL is a reshare, resolve the canonical original post first — see "Reshare gotcha" below — comments live on the original, not the reshare's activity id. 2. **Fetch the full comment tree.** Call `lib.ApifyClient.fetch_post_comments(post_id=<post_urn or resolved canonical id>, max_items=100)` Comments come back sorted by most relevant, which is what surfaces the reply threads the parentComment rule needs; pass `sort_order="most recent"` if the user explicitly wants the newest first. If `APIFY_TOKEN` is not set, ask the user to paste the comment list (name + text per comment is enough;