| name | kol-discovery |
| description | Find Key Opinion Leaders (KOLs) in a given domain by combining web research with LinkedIn post search. Given a company/idea and target domain, generates authority keywords, searches LinkedIn posts to find prolific authors with high engagement, and merges with web-researched influencers. Use when someone wants to "find influencers in X space" or "who are the KOLs for Y industry."
|
| tags | ["outreach"] |
KOL Discovery
Find Key Opinion Leaders in any domain by searching LinkedIn posts for prolific, high-engagement authors and merging with web-researched influencers.
Core principle: Search for authority/thought-leadership keywords, not pain-language. We want people who shape conversation in the space — conference speakers, newsletter writers, podcast hosts, and prolific LinkedIn posters.
Phase 0: Intake
Ask the user these questions:
Domain & Audience
- What does your company/product do? What space are you in?
- What specific domain or topic are the KOLs you want to find expert in?
- Who is your target audience? (The people the KOLs influence)
- Any KOLs you already know about? (LinkedIn URLs — these become the baseline)
- Anyone to EXCLUDE? (Competitors, your own team, irrelevant voices)
Phase 1: Generate Domain Keywords
Based on intake, generate 15-25 topic/authority keywords. These are NOT pain-language — they're the terms thought leaders use when sharing expertise:
- 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":
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:
- Keyword search —
apimaestro/linkedin-posts-search-scraper-no-cookies for each domain keyword
- Author aggregation — Group posts by author, compute engagement metrics
- Scoring — Composite KOL score: engagement volume (log-scaled) + consistency (post count) + quality (avg engagement) + relevance (keyword breadth) + web research bonus
- Merge — Combine post-data KOLs with web-researched KOLs, flag overlaps
- 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:
| Column | Description |
|---|
| Rank | Overall rank by KOL Score |
| Name | Full name |
| LinkedIn URL | Profile link |
| Headline | From LinkedIn |
| KOL Score | Composite score |
| Total Posts | Posts found in search |
| Total Reactions | Sum of reactions across posts |
| Total Comments | Sum of comments across posts |
| Avg Engagement | Average reactions+comments per post |
| Top Post URL | Highest engagement post |
| Top Post Preview | First 100 chars of top post |
| Source | post-data / web-research / both |
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
