KOL Content Monitor
Track what Key Opinion Leaders in your space are writing about. Surface trending narratives early — before they peak — so your team can join the conversation at the right time with relevant content.
Core principle: For seed-stage teams, the fastest path to content distribution is riding a wave that's already breaking, not creating one from scratch.
When to Use
- "What are the top voices in [our space] posting about?"
- "What topics are trending on LinkedIn in [industry]?"
- "I want to know what content is resonating before I write anything"
- "Track [list of founders/experts] and tell me what they're saying"
- "Find trending narratives I can contribute to"
Phase 0: Intake
KOL List
- Names and LinkedIn URLs of KOLs to track (if known)
- If unknown: use
kol-discovery skill first to build the list
- Twitter/X handles for the same KOLs (optional but recommended for full picture)
- Any specific topics/keywords you care about? (for filtering noisy feeds)
Scope
- How far back? (default: 7 days for weekly monitor, 30 days for first run)
- Minimum engagement threshold to include a post? (default: 20 reactions/likes)
Save config to clients/<client-name>/configs/kol-monitor.json.
{
"kols": [
{
"name": "Lenny Rachitsky",
"linkedin": "https://www.linkedin.com/in/lennyrachitsky/",
"twitter": "@lennysan"
},
{
"name": "Kyle Poyar",
"linkedin": "https://www.linkedin.com/in/kylepoyar/",
"twitter": "@kylepoyar"
}
],
"days_back": 7,
"min_reactions": 20,
"keywords": ["GTM", "growth", "AI", "outbound", "founder"],
"output_path": "clients/<client-name>/intelligence/kol-monitor-[DATE].md"
}
Phase 1: Scrape LinkedIn Posts
Run linkedin-profile-post-scraper for all KOL LinkedIn profiles:
python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
--profiles "<url1>,<url2>,<url3>" \
--days <days_back> \
--max-posts 20 \
--output json
Filter results: only include posts with reactions ≥ min_reactions.
Phase 2: Scrape Twitter/X Posts
Run twitter-scraper for each handle:
python3 skills/twitter-scraper/scripts/search_twitter.py \
--query "from:<handle>" \
--since <YYYY-MM-DD> \
--until <YYYY-MM-DD> \
--max-tweets 20 \
--output json
Filter: only include tweets with likes ≥ min_reactions / 2 (Twitter engagement is lower than LinkedIn).
Phase 3: Topic Clustering
Group all posts across all KOLs by topic/theme:
Clustering approach:
- Extract the main topic from each post (1-3 word label)
- Group similar topics together
- Count: how many KOLs touched this topic? How many total posts?
- Rank by: total engagement (sum of reactions/likes across all posts on that topic)
This surfaces topics with broad consensus (multiple KOLs talking about it) vs. individual takes.
Signal types to flag:
| Signal | Meaning | Example |
|---|
| Convergence | 3+ KOLs on same topic in same week | Multiple founders posting about "AI SDR fatigue" |
| Spike | Topic that 2x'd in volume vs last week | Suddenly everyone's talking about [new thing] |
| Underdog | 1 KOL posting about topic nobody else covers | Potential early-mover opportunity |
| Controversy | Posts with high comment/reaction ratio | Debate you could weigh in on |
Phase 4: Output Format
# KOL Content Monitor — Week of [DATE]
## Tracked KOLs
[N] KOLs | [N] LinkedIn posts | [N] tweets | Period: [date range]
---
## Trending Topics This Week
### 1. [Topic Name] — CONVERGENCE SIGNAL
- **KOLs discussing:** [Name 1], [Name 2], [Name 3]
- **Total posts:** [N] | **Total engagement:** [N] reactions/likes
- **Trend direction:** ↑ New this week / ↑↑ Growing / → Stable
**Best posts on this topic:**
> "[Post excerpt — first 150 chars]"
— [Author], [Date] | [N] reactions
[LinkedIn URL]
> "[Tweet text]"
— [@handle], [Date] | [N] likes
[Twitter URL]
**Content opportunity:** [1-2 sentences on how to contribute to this conversation]
---
### 2. [Topic Name]
...
---
## High-Engagement Posts (Top 5 This Week)
| Post | Author | Platform | Engagement | Topic |
|------|--------|----------|------------|-------|
| "[Preview...]" | [Name] | LinkedIn | [N] reactions | [topic] |
...
---
## Emerging Topics to Watch
Topics picked up by 1 KOL this week — too early to call a trend but worth tracking:
- [Topic] — [KOL name] — [brief description]
- [Topic] — ...
---
## Recommended Content Actions
### This Week (Ride the Wave)
1. **[Topic]** is peaking — ideal moment to publish your take. Suggested angle: [angle]
2. **[Controversy]** is generating debate — consider a nuanced response post. Your positioning: [suggestion]
### Next Week (Get Ahead)
1. **[Emerging topic]** is early-stage — write something now before it gets crowded.
Save to clients/<client-name>/intelligence/kol-monitor-[YYYY-MM-DD].md.
Phase 5: Build Trigger-Based Content Calendar
Optional: from the monitor output, propose a content calendar entry for each "Ride the Wave" opportunity:
Topic: [topic]
Best post format: [LinkedIn insight post / tweet thread / blog]
Suggested hook: [hook]
Supporting points: [3 bullets from your product/experience]
Ideal publish date: [within 3 days of peak]
Scheduling
Run weekly (Friday afternoon — catches the week's peaks and gives weekend to draft):
0 14 * * 5 python3 run_skill.py kol-content-monitor --client <client-name>
Cost
| Component | Cost |
|---|
| LinkedIn post scraping (per profile) | ~$0.05-0.20 (Apify) |
| Twitter scraping (per run) | ~$0.01-0.05 |
| Total per weekly run (10 KOLs) | ~$0.50-2.00 |
Tools Required
- Apify API token —
APIFY_API_TOKEN env var
- Upstream skills:
linkedin-profile-post-scraper, twitter-scraper
- Optional upstream:
kol-discovery (to build initial KOL list)
Trigger Phrases
- "What are the top voices in [space] posting about this week?"
- "Track my KOL list and give me content ideas"
- "Run KOL content monitor for [client]"
- "What's trending on LinkedIn in [industry]?"
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