--- name: kol-content-monitor description: > 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. tags: [monitoring] --- # 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). ```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": "kol-monitor-[DATE].md" } ``` ## Phase 1: Scrape LinkedIn Posts Run `linkedin-profile-post-scraper` for all KOL LinkedIn profiles: ```bash python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \ --profiles ",," \ --days \ --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: ```bash python3 skills/twitter-mention-tracker/scripts/search_twitter.py \ --query "from:" \ --since \ --until \ --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: | 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 ```markdown # 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): ```bash 0 14 * * 5 python3 run_skill.py kol-content-monitor --client ``` ## 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-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]?"