Imported from gooseworks-ai/goose-skills (skills/monitoring/composites/kol-content-monitor/SKILL.md). Install upstream with npx skills add gooseworks-ai/goose-skills --skill kol-content-monitor. Copyright stays with the author.

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

1. Names and LinkedIn URLs of KOLs to track (if known)
   • If unknown: use kol-discovery skill first to build the list
2. Twitter/X handles for the same KOLs (optional but recommended for full picture)
3. Any specific topics/keywords you care about? (for filtering noisy feeds)

Scope

4. How far back? (default: 7 days for weekly monitor, 30 days for first run)
5. Minimum engagement threshold to include a post? (default: 20 reactions/likes)

Save config to the current working directory as kol-monitor.json (or user-specified path).

{
  "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": "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-mention-tracker for each handle:

python3 skills/twitter-mention-tracker/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:

1. Extract the main topic from each post (1-3 word label)
2. Group similar topics together
3. Count: how many KOLs touched this topic? How many total posts?
4. 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:

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 the current working directory as kol-monitor-[YYYY-MM-DD].md (or user-specified path).

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

Tools Required

• Apify API token — APIFY_API_TOKEN env var
• Upstream skills: linkedin-profile-post-scraper, twitter-mention-tracker
• 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]?"