Kol Content Monitor Agent Skill
> Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/gooseworks-ai/goose-skills --skill kol-content-monitorWhat comes with it
377 bytes besides the instructionThe instruction itself
16 sections, as written by the authorKOL 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-discoveryskill 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 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:
- 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 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
| 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_TOKENenv 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]?"
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How to use it
Take gooseworks-ai/kol-content-monitor from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.