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/linkedin-thread-monitor

@321dda7

Track which of your LinkedIn comments earned author replies. Flags the 6-24h warm-reply window where thread momentum peaks, classifies threads as hot/warm/cool/dormant, and routes warm ones to linkedin-reply-handler for follow-up drafts. Powered by Apify, no LinkedIn login. Triggers on "what threads need follow-up", "author replied", "monitor my comments". Not for analyzing likers on a post (use linkedin-engager-analytics).

Use this Skill: https://skilld.dev/gh/sergebulaev/linkedin-skills/linkedin-thread-monitor

This session only. Nothing lands on disk.

referencesthread-timing.md

≈943 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Thread Timing Matrix

Thread stage classification

Time since user's comment Time since last reply Stage Priority
<6h any Watch (author may still reply) Low — check back
6-24h author replied <2h ago Hot — respond within 90 min HIGH
6-24h author replied 2-12h ago Warm — respond within 2h HIGH
6-24h no author reply Cold — skip —
24-72h author replied recently Cool — respond within 4h Medium
24-72h no author reply Dormant —
>72h any Dormant — switch to DM Medium (if inbound-quality)

The warm-reply window explained

Real example from 2026-04:

  • 14:27 UTC: Serge posted comment on a CEO's post ("moat moved from tools to taste")
  • 12:06 UTC next day (~22h later): the author replied personally ("How are you building that conviction muscle with your team?")
  • 16:24 UTC that day (~28h after original comment, ~4h after the author's reply): Serge replied with his answer

This is the exact window the skill targets. Miss it by 12+ hours and the reply lands in a dormant thread where the author doesn't get the notification prominently.

First 60 min on own posts

Different metric — how fast the USER replies to comments on their own posts:

  • Target: every comment replied to within 5-15 min during first 60 min
  • Each reply within 90 min fires ~90% boost on that thread
  • 3+ substantive comments in first 30 min = second algo push

Engagement half-life

  • 0-6h: 70% of all eventual reactions/comments happen here
  • 6-24h: 25% — the long tail
  • 24-72h: 5% — trickle
  • >72h: essentially dead (<1% of eventual engagement)

Rule: when thread dies, switch to DM

If a thread is dormant (>72h since last turn) but the counterpart was high-quality, don't reply in thread — the post won't surface their notification. Instead, draft a DM:

[Name] — circling back on our thread about [specific topic from thread].

[Your one new thought or data point].

Worth a 15-min conversation? Tuesday or Thursday this week if yes.

The DM should reference the thread specifically, not be a generic pitch.

Anti-patterns

  • Chaining 3+ replies under one top comment (looks like thread hijack)
  • Replying after 72h in the thread itself (low visibility, looks desperate)
  • Generic "catching up on this thread" without a new thought
  • DMing before the public thread closes naturally (skips the earned step)
  • Replying to replies OF replies (LinkedIn flattens — it doesn't nest that deep)

Publishing-adjacent timing windows (own posts)

Phase Window Action
Warm-up 15 min BEFORE publishing Leave 3-5 substantive comments on others' posts
Critical First 30 min AFTER publishing Reply to every comment within minutes; distribution contracts if dead
Seeding 15-30 min after posting Leave 3-5 bonus comments on your own post to create thread depth
Visibility bump Reply within 1st hour +35% visibility lift (author-reply signal)

Peer engagement (safe pattern, not a pod)

A safe peer group is 5-8 people in adjacent fields who actually read each other's work and comment only when they have something substantive to say.

Distinguishes from pods by:

  • Varied timing (no fixed daily slot)
  • Varied commenters per post (not the same 6 people every time)
  • Comment substance >10 words, with new angles
  • No reciprocity obligation

Pod detection catches:

  • Same accounts engaging at the same clock minute daily (e.g., 9:01 AM)
  • 15+ comments landing within a 90-second window
  • Identical like/comment pattern across every post

Real penalty observed: one creator dropped from 8,500 to 340 impressions overnight after pod detection. Recovery: 6-8 weeks.

Source: SKILL.md on GitHub

1 warning15d3 checks · Risk SAFE
  • Gen Agent Trust Hub15d

    The skill is designed to monitor LinkedIn interactions using the Apify API. It effectively manages security risks by avoiding direct LinkedIn login and includes robust instructions for handling untrusted external data. A low-severity finding is noted regarding the inherent risk of indirect prompt injection from processing third-party social media content.

  • Socket15d

    No alerts

  • Snyk15d

    Risk: MEDIUM · 1 issue

Signed by skilld at 321dda7. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 2 weeks ago

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