linkedin-engagement-monitor

LinkedIn Engagement Monitor

Two read-side workflows, one skill.

1. Thread monitoring. Track which of your comments earned author replies, draft timely follow-ups, flag the 6-24h Kevin Payne window where thread momentum is highest.
2. Engager analytics. Pull every liker and commenter on any post, group them by ICP fit, surface peer / aspirational / prospect tiers, and feed the result into your DM or outreach queue.

Both workflows depend on APIFY_TOKEN. Without it, fall back to user-paste.

When to use

• Daily: "What threads need follow-up today?"
• After posting a batch of comments: "Check back in 6 hours"
• When an author replied personally: "Draft the response"
• After publishing a post: "Who actually engaged? Are they ICP?"
• Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size"

Input

• Mode 1 (thread monitoring): your LinkedIn handle (last path segment of profile URL).
• Mode 2 (engager analytics): one or more LinkedIn post URLs.
• Optional for both: ICP definition (target titles, company size, industry).

Mode 1. Thread monitoring

Output

Daily report

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

For each warm thread

• Thread preview (last 3 turns)
• Suggested response (drafted via linkedin-reply-handler)
• Reaction target (the specific reply URN, not the post)
• Priority (high / medium / low)

Weekly roll-up

• Total comments posted
• Author-reply rate (target 15%+)
• Conversion to DM (when thread closes warm)

Steps (Mode 1)

1. Fetch user's recent comments. If APIFY_TOKEN is set, call lib.ApifyClient.fetch_user_recent_comments(username=<your-handle>, result_limit=30). Each item already includes the parent post body, post URL, post author, and reaction stats. If APIFY_TOKEN is not set, ask the user to list (or paste) the URLs of comments they've posted in the last 72h.
2. For each comment posted in last 72h: check the parent post's comment tree (use fetch_post_comments(post_id=..., scrape_replies=True)) for:
   • Replies to the user's comment
   • Whether the author posted any of those replies
   • Timestamps (time since user's comment, time since latest reply)
3. Classify stage:
   • Hot (<6h): author just replied. Respond within 90 min for max thread momentum
   • Warm (6-24h): the Kevin Payne window. Author replies most happen here
   • Cool (24-72h): still respondable but lower velocity
   • Dormant (>72h): don't reply in thread. Consider DM
4. Draft responses for warm threads using linkedin-reply-handler.
5. Flag suspicious patterns:
   • Author replied but also deleted someone else's comment (author is actively moderating, tread carefully)
   • Commenter is in thread self-promoting (your reply shouldn't engage them)
6. DM routing: if thread is dormant but the author engaged meaningfully, draft a DM that references the thread specifically.

Kevin Payne window

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.

• 0-6h: 70% of author replies happen here if they're going to happen
• 6-24h: ~25% of author replies, but these are higher-quality (author took time to think)
• >24h: thread rarely produces new author engagement

Follow-up timing:

• If author replied in 0-6h window: respond within 90 minutes
• If author replied in 6-24h window: respond within 2 hours (they're still checking)
• If author replied >24h: respond within 4 hours before thread goes cold

Mode 2. Engager analytics

Output

Engager roster

#	Type	Name	Title	Company	Profile	ICP tier
1	commenter	Jordan Boynton	Director	Cerberus Cosmetics	linkedin.com/in/...	Prospect
2	commenter	Adaita Mishra	Senior PM	Atlassian	linkedin.com/in/...	Aspirational
3	liker	Lara Acosta	Founder	Lara LLC	linkedin.com/in/...	Peer

Tier breakdown

Tier	Definition	Count	% of total
Peer	Founder / operator at company in same niche, 5-50 employees	12	24%
Aspirational	Senior leader at 50+ company in adjacent niche	9	18%
Prospect	Director / C-suite at company matching ICP	18	36%
Other	Doesn't fit any tier	11	22%

Action lists

• Follow back (peers worth reciprocal engagement): top 5 by activity
• Comment-drop targets (aspirational creators with their own posts): top 5
• DM-able prospects (with the rationale): top 5 with one-line opener seed

Steps (Mode 2)

1. Fetch engagers. Call lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100). Returns a list of dicts with type ("commenters" | "likers"), name, subtitle (job title + company), url_profile, content (comment text if commenter), datetime. Cost is roughly $0.005 per engager-record.
2. Parse subtitle into structured fields. The subtitle typically reads "Director at Cerberus Cosmetics" or "Founder & CEO at LawVu". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder).
3. Score ICP fit. Use the user's supplied ICP rules:
   • Title match (regex or keyword list)
   • Company size proxy (look up via the user's CRM if integrated, else mark Unknown)
   • Industry match (parse company name + subtitle keywords)
4. Assign tier.
   • Peer: founder / operator at similar-stage company in same niche
   • Aspirational: senior leader (Director+) at larger company in adjacent niche
   • Prospect: title in ICP target list AND company in ICP target list
   • Other: no match
5. Produce action lists.
   • Follow back: peers with active posting (heuristic: appears as author in fetch_user_recent_comments of any team member)
   • Comment-drop targets: aspirational tier
   • DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to . Curious. Are you currently ?")
6. Optional cross-post analysis. If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).

Inbound-quality signals (apply to both modes)

High-quality engager = worth the follow-up:

• Founder / operator title in profile
• Company in user's ICP
• Active posting history (not just reactions)
• Mutual 2nd-degree connections >10
• Prior thoughtful comments on user's posts

Low-quality = skip:

• Generic praise with no specifics
• Template language ("I'd love to hop on a quick call")
• Profile is sales / agency with no operator history
• Same comment across many creators' posts

Hard rules

• Never reply to a reply later than 72h after the thread's last turn. Switch to DM.
• Never chain 3+ replies under one comment (thread spam).
• If the author deleted their reply, do not reply. They reconsidered.
• Don't DM a warm thread before first replying publicly (skips a step).
• Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the "thirsty" pattern.
• Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy.

Examples

Mode 1. Thread monitoring

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.

Mode 2. Engager analytics

Input: analyze engagers on https://www.linkedin.com/posts/satyanadella_..., max 100

Output:

• 50 commenters fetched ($0.25)
• Tier split: 6 Peer / 14 Aspirational / 18 Prospect / 12 Other
• 3 cross-post engagers detected (also engaged with my post 2 weeks ago)
• Top 5 DM-able prospects with one-line openers attached

Cost accounting

Action	Apify call	Cost (free tier)
Daily thread sweep (1 user, ~30 comments)	fetch_user_recent_comments once	$0.005
Per-warm-thread context	fetch_post_comments(scrape_replies=True)	$0.005 each
Engager analytics on one post (50 engagers)	fetch_post_engagers(max_items=50)	$0.25
Engager analytics on one post (200 engagers)	fetch_post_engagers(max_items=200)	$1.00

A typical creator running this skill 5 days/week with 1 engager-analytics run/week stays well under the $5 free monthly credit.

Files

• SKILL.md — this file
• references/thread-timing.md — the timing matrix with examples (Mode 1)

Related skills

• linkedin-reply-handler — drafts the actual follow-up message for warm threads
• linkedin-comment-drafter — drafts the initial comment that starts threads