Kol Discovery

• Industry terms — "freight tech", "supply chain innovation"
• Thought leadership signals — "lessons learned in logistics", "future of dispatch"
• Conference/event terms — "supply chain summit keynote"
• Content creator signals — "newsletter freight", "podcast logistics"

Also generate:

• KOL title keywords — titles that signal thought leadership (vp, founder, analyst, editor, host)
• Vendor exclusion keywords — titles to filter out (software engineer, recruiter, saas)
• Domain relevance keywords — core industry terms for relevance scoring

Present keywords to user for approval before running.

Save config in the current working directory or wherever the user prefers:

Config JSON structure:

{
  "client_name": "example",
  "domain_keywords": ["\"freight tech\" thought leadership", "supply chain innovation"],
  "exclusion_patterns": ["hiring.*position", "we.re recruiting"],
  "kol_title_keywords": ["vp", "founder", "analyst", "editor", "host"],
  "vendor_exclude_keywords": ["software engineer", "saas", "recruiter"],
  "domain_relevance_keywords": ["freight", "logistics", "supply chain"],
  "country_filter": "",
  "max_posts_per_keyword": 50,
  "min_posts": 2,
  "min_total_engagement": 50,
  "top_n_kols": 50
}

Phase 2: Run KOL Discovery Pipeline

python3 skills/kol-discovery/scripts/kol_discovery.py \
  --config kol-discovery.json \
  --output-dir . \
  [--test] [--web-kols kol-web-kols.json] [--yes]

Flags:

• --config (required) — path to client config JSON
• --output-dir — directory for output CSV (default: current working directory)
• --test — limit to 5 keywords (validation run)
• --web-kols — path to web-researched KOL JSON (agent generates this)
• --yes — skip cost confirmation prompts
• --max-runs — override Apify run limit

What the script does:

1. Keyword search — apimaestro/linkedin-posts-search-scraper-no-cookies for each domain keyword
2. Author aggregation — Group posts by author, compute engagement metrics
3. Scoring — Composite KOL score: engagement volume (log-scaled) + consistency (post count) + quality (avg engagement) + relevance (keyword breadth) + web research bonus
4. Merge — Combine post-data KOLs with web-researched KOLs, flag overlaps
5. Export — Ranked CSV

Cost estimate: ~$0.10 per keyword. Full run with 20 keywords: ~$2-3.

Always run with --test first.

Phase 2b: Web Research (Agent-Driven)

Before or alongside the script, do web research to find known KOLs:

• Search for "top [industry] influencers on LinkedIn"
• Find conference speakers, newsletter authors, podcast hosts
• Check industry publications for frequent contributors

Save as JSON in the current working directory:

[
  {
    "name": "Jane Doe",
    "linkedin_url": "https://www.linkedin.com/in/janedoe/",
    "source": "FreightWaves conference speaker 2025",
    "notes": "Hosts weekly logistics podcast"
  }
]

Pass to script via --web-kols.

Phase 3: Review & Refine

Present results:

• Top 20 KOLs — rank, name, headline, KOL score, total engagement, top post
• Source breakdown — how many from post-data vs web-research vs both
• Keyword performance — which keywords surfaced the most KOLs

Common adjustments:

• Too many irrelevant authors — refine domain keywords, add exclusion patterns
• Missing known KOLs — add more keyword variants, expand web research
• Too few results — lower min_posts or min_total_engagement thresholds

Phase 4: Output

CSV exported to the current working directory:

Tools Required

• Apify API token — set as APIFY_API_TOKEN in .env
• Apify actors used:
  • apimaestro/linkedin-posts-search-scraper-no-cookies (keyword search)

Example Usage

Trigger phrases:

• "Find KOLs in the freight/logistics space"
• "Who are the influencers in [industry]?"
• "Discover thought leaders for [domain]"
• "Run KOL discovery for [client]"

With existing config:

python3 skills/kol-discovery/scripts/kol_discovery.py \
  --config clients/example/configs/kol-discovery.json \
  --output-dir clients/example/leads --yes