Install
openclaw skills install linkedin-thread-engagementTracks your LinkedIn comments for author replies within 72h, flags high-value engagement windows, and drafts timely follow-ups to maximize thread momentum.
openclaw skills install linkedin-thread-engagementThe engagement compounding layer. Tracks which of the user's comments earned author replies, drafts timely follow-ups, and flags the 6-24 hour window where thread momentum is highest.
| Posted | Author | Post | Comment | Reply? | Stage | Action |
|---|---|---|---|---|---|---|
| 18h ago | Kevin Payne | LawVu | "moat moved to taste" | ✅ Kevin replied 14h ago | Warm (6-24h window) | Reply now |
| 22h ago | Dharmesh Shah | HubSpot | "integration depth moat" | No | Cold | Skip |
| 3h ago | Felix T. | Rezolve | "twin economies" | No | Watch | Check in 3h |
linkedin-reply-handler)/linkedin/profile-comments.linkedin-reply-handler (which adapts to the active backend per lib.active_backend() — Publora auto-posts, manual mode returns copy-paste, DIY invokes custom poster).Named after the real 2026-04 data point: Kevin Payne (LawVu CEO) replied to Serge's comment 22h after the original post. This is the sweet spot.
Follow-up timing:
High-quality commenter = worth the follow-up:
Low-quality = skip:
Input: monitor sbulaev profile, last 24h
Output:
- 1 warm thread: Kevin Payne replied 14h ago on LawVu post. Current stage: Warm (8-24h). Suggested response ready. Action: post within 2 hours.
- 8 cold threads (no author engagement). Skip.
- 3 watching threads (<6h old, author may still reply). Check again in 3-6h.
SKILL.md — this filereferences/thread-timing.md — the timing matrix with exampleslinkedin-reply-handler — drafts the actual follow-up messagelinkedin-comment-drafter — drafts the initial comment that starts threads