KOL Content Monitor

9 steps · start to finish.

1. 1
   Step 1

   Environment Setup

   ▶

   Create the output directory, load the monitor config, and verify the tools and credentials needed for the selected sources.

   mkdir -p /app/results
   test -f "${CONFIG_PATH:-kol-monitor.json}" || {
     echo "ERROR: missing KOL monitor config"
     exit 1
   }
   command -v python3 >/dev/null || {
     echo "ERROR: python3 is required"
     exit 1
   }
   

   Validate the config before scraping. It must include at least one KOL with a LinkedIn URL or Twitter/X handle, days_back, min_reactions, and an output path. If LinkedIn profiles are present and the upstream scraper requires Apify, verify APIFY_API_TOKEN is set without printing the secret.

2. 2
   Step 2

   Intake and Config Normalization

   ▶

   Read config_path and normalize each KOL entry into a consistent schema:

3. 3
   Step 3

   Scrape LinkedIn Posts

   ▶

   Run linkedin-profile-post-scraper for all configured LinkedIn profiles:

4. 4
   Step 4

   Scrape Twitter/X Posts

   ▶

   For each configured handle, run twitter-mention-tracker over the same date window:

5. 5
   Step 5

   Topic Clustering

   ▶

   Group all retained posts by topic or theme:

6. 6
   Step 6

   Generate the Monitor Report

   ▶

   Write /app/results/kol-monitor-report.md with:

7. 7
   Step 7

   Build Trigger-Based Content Calendar

   ▶

   When include_calendar=true, write /app/results/content-calendar.md with one entry for each strong "Ride the Wave" opportunity:

8. 8
   Step 8

   Iterate on Errors (max 3 rounds)

   ▶

   If scraping, clustering, or report validation fails, perform at most 3 rounds of targeted fixes:

9. 9
   Step 9

   Scheduling

   ▶

   For a weekly Friday afternoon monitor, schedule the equivalent command after validating the config: