find-comment-targets

---

name: find-comment-targets description: Finds LinkedIn target posts to comment on. Search strategies — by topic (author's expertise clusters), by ICP (potential customers), by networking (thought leaders in the niche). Helps plan the engagement routine. disable-model-invocation: true argument-hint: [Author] [--strategy topic|icp|networking|all] allowed-tools: Read, Grep, Glob

Find Comment Targets

Search for and recommend target LinkedIn posts to comment on.

Input

• $0 — author name (Seva, Kirill)
• --strategy — search strategy (default: all)
  • topic — posts on topics from the author's expertise clusters
  • icp — posts by people in the ICP (potential customers)
  • networking — posts by thought leaders for networking
  • all — all three strategies

Required files

1. Author DIP: Knowledge-Base/01-Identity-Profiles/[Author]-DIP.md → Expertise Clusters, Topics, Target audience

2. Algorithm-Intelligence: Knowledge-Base/02-LinkedIn-Algorithm/Algorithm-Intelligence.md → Engagement rules, Comments Train Profile

3. ICP Reference: ICP-Reference.md → Target industries, budgets, characteristics

4. Current-Trends: Knowledge-Base/03-Viral-Trends/Current-Trends.md → What's trending now

Why comment strategically

Fact	Source
Commenting on 5–10 posts after publication → +55% profile views	Algorithm-Intelligence
Comments Train Profile — what you comment on teaches the algorithm whom to show you to	360Brew model
Thoughtful comments in your niche → +20% reach for your own posts	Buffer / SocialInsider
Mix: 30% team, 70% external — to avoid creating an engagement pod	Comments-Engine

Pipeline

Step 1: Load DIP + pick strategy

From the DIP, extract:

• Expertise Clusters → topics to search for
• Target audience → who we're looking for
• Stories Bank → what we can concretely add

Step 2: Define search criteria

Strategy: Topic (posts on topic)

Search for posts on topics from the author's Expertise Clusters:

Author	Key topics for search
Seva	AI agents in marketing, performance marketing automation, founder journey, AI-first teams
Kirill	Growth marketing, creative testing, attribution, campaign automation
Denis	Product management, data-driven product decisions, CPO systems
Ella	Content strategy, marketing operations, team workflows

Criteria for a good post to comment on:

• Topic overlaps with one of the author's expertise clusters
• Post author = relatable peer (not a corporate account)
• The post sparked a discussion (has comments)
• There's room to add a unique perspective with specifics

Strategy: ICP (potential customers)

Search for posts by people who:

• Work in ICP industries (Consumer Software, Fintech B2C, Consumer Services, etc.)
• Role: Head of Growth, VP Marketing, CMO, Performance Marketing Lead
• Write about: attribution challenges, marketing automation, scaling paid channels, LTV optimization
• Ad budget: $50K+/month (inferred from company scale)

Do NOT sell in comments! The goal is to become a visible expert, not to pitch.

Strategy: Networking (thought leaders)

Search for posts from:

• Industry thought leaders in performance marketing, MarTech, AI
• Podcast hosts and event organizers in our niche
• Founders of companies with similar missions
• Journalists and analysts covering MarTech / AI

Step 3: Recommendations

For each recommended post / post type:

### Target [N]

**Who to look for:** [profile description / type of content]
**Topics:** [keywords for search]
**Strategy:** [topic / icp / networking]
**Why:** [how it connects to the author's DIP]
**Possible angle:** [what specifics from the DIP can be added]
**Sample comment:** [1–2 sentence sketch]

Step 4: Engagement Plan

## Engagement Plan for [Author]

### Daily routine (recommended)
- 5–10 comments per day
- Mix: 30% team, 70% external
- Timing: before and after publishing your own post

### Distribution across strategies
- Topic (expertise): 40% — reinforces the profile in the niche
- ICP (customers): 30% — visibility with the target audience
- Networking (leaders): 30% — connections with industry leaders

### Key topics for this week
(based on Current-Trends + Algorithm-Intelligence)
1. [topic 1] — why it's relevant now
2. [topic 2] — why it's relevant now
3. [topic 3] — why it's relevant now

Output

1. List of recommended post types with search criteria
2. Engagement Plan with strategy distribution
3. Key topics of the week for commenting
4. Sample angles for each target type

Important

• This skill does NOT log into LinkedIn. It builds strategy and search criteria.
• The user finds concrete posts themselves and can use /comment-on-post to write a comment.
• Refresh recommendations weekly (via /weekly-update).