kol-engager-icp

KOL Engager ICP

Find ICP-fit leads by scraping engagers from KOL posts on LinkedIn. This is the second half of the KOL pipeline — given KOLs (from kol-discovery or manually), it finds their best post, scrapes who engaged, and filters for your ICP.

Core principle: 1 post per KOL. Pick the most relevant, highest-engagement post from the last 30 days. This controls costs while maximizing lead quality.

Phase 0: Intake

Ask the user these questions:

ICP Criteria

1. What does your product/service do?
2. Topic keywords for post relevance filtering (3-5 terms the KOL posts should be about)
3. Target industries/verticals
4. Target job titles/roles (e.g., "VP Operations", "Head of Logistics")
5. Titles to EXCLUDE (e.g., "Software Engineer", "Data Scientist")
6. Competitors to filter out
7. Geographic focus (e.g., "United States")

KOL Input

8. KOL list — LinkedIn profile URLs (from kol-discovery output or manual list)

Save config:

skills/kol-engager-icp/configs/{client-name}.json

Config JSON structure:

{
  "client_name": "example",
  "topic_keywords": ["freight automation", "dispatch operations"],
  "topic_patterns": ["freight.*automat", "dispatch.*oper"],
  "icp_keywords": ["freight", "logistics", "3pl"],
  "target_titles": ["vp operations", "head of logistics", "coo"],
  "exclude_titles": ["software engineer", "data scientist"],
  "tech_vendor_keywords": ["competitor-name", "saas founder"],
  "country_filter": "United States",
  "kol_urls": ["https://www.linkedin.com/in/kol-1/"],
  "days_back": 30,
  "max_posts_per_kol": 20,
  "max_kols": 10,
  "max_enrichment_profiles": 200,
  "mode": "standard"
}

Phase 1: Run the Pipeline

python3 skills/kol-engager-icp/scripts/kol_engager_icp.py \
  --config skills/kol-engager-icp/configs/{client-name}.json \
  [--test] [--probe] [--yes] [--kols "url1,url2"]

Flags:

• --config (required) — path to client config JSON
• --test — limit to 3 KOLs, 50 enrichment profiles
• --probe — test engager scraping with one post URL and exit
• --yes — skip cost confirmation prompts
• --kols — override KOL URLs from config (comma-separated)
• --max-runs — override Apify run limit

Pipeline Steps

Step 1: Scrape KOL posts — For each KOL, fetch recent posts (last 30 days, max 20 posts to scan) using harvestapi/linkedin-profile-posts.

Step 2: Select best post per KOL — Filter posts by topic_keywords/topic_patterns relevance, then pick the ONE with highest engagement (reactions + comments). Result: 1 post URL per KOL.

Step 3: Scrape engagers — Use harvestapi/linkedin-company-posts with scrapeReactions: true, scrapeComments: true to get reactors and commenters from each selected post.

Step 4: Pre-filter before enrichment — Score engagers by position:

• +3 Commenter (higher intent)
• +2 Position matches ICP keywords
• +2 Position matches target titles
• -5 Position matches exclude titles or vendor keywords
• +1 Engaged on multiple posts
• Keep only score > 0, cap at max_enrichment_profiles

Step 5: Enrich — supreme_coder/linkedin-profile-scraper in batches of 25. Apply country filter after.

Step 6: ICP classify & export — Classify as Likely ICP / Possible ICP / Unknown / Tech Vendor. Export CSV.

Hard Caps

Parameter	Test	Standard	Full
KOLs processed	3	10	20
Posts selected per KOL	1	1	1
Max reactions scraped	all	all	all
Max profiles enriched	50	200	500
Est. total cost	~$0.50	~$1.50-2	~$5-8

Probe Mode

Run --probe first to verify engager scraping works:

python3 skills/kol-engager-icp/scripts/kol_engager_icp.py \
  --config skills/kol-engager-icp/configs/{client-name}.json --probe

This scrapes posts from the first KOL, selects the best post, scrapes engagers from it, and prints a sample. No enrichment, no CSV.

Phase 2: Review & Refine

Present results:

• Per-KOL breakdown — which KOL's post generated the most leads
• Pre-filter stats — how many engagers passed the position filter
• ICP breakdown — counts by tier
• Top 15 leads — name, role, company, KOL source, engagement type

Common adjustments:

• Too many tech vendors — add terms to tech_vendor_keywords
• Missing ICP leads — broaden icp_keywords or target_titles
• Low engagement posts selected — adjust topic_keywords to be less restrictive
• Too expensive — lower max_enrichment_profiles or switch to test mode

Phase 3: Output

CSV exported to skills/kol-engager-icp/output/{client-name}-kol-engagers-{date}.csv:

Column	Description
Name	Full name
LinkedIn Profile URL	Profile link
Role	Parsed from headline
Company Name	Parsed from headline
Location	From enrichment
KOL Source	Which KOL's post they engaged with
Post URL	Link to the specific post
Engagement Type	Comment or Reaction
Comment Text	Their comment (personalization gold)
ICP Tier	Likely ICP / Possible ICP / Unknown / Tech Vendor
Pre-Filter Score	Priority score from Step 4

Tools Required

• Apify API token — set as APIFY_API_TOKEN in .env
• Apify actors used:
  • harvestapi/linkedin-profile-posts (KOL post scraping)
  • harvestapi/linkedin-company-posts (engager scraping from posts)
  • supreme_coder/linkedin-profile-scraper (profile enrichment)

Example Usage

Trigger phrases:

• "Find leads from KOL audiences in [industry]"
• "Scrape engagers from these KOL posts"
• "Run kol-engager-icp for [client]"
• "Who is engaging with [KOL name]'s content?"

After kol-discovery:

# Use KOL URLs from discovery output
python3 skills/kol-engager-icp/scripts/kol_engager_icp.py \
  --config skills/kol-engager-icp/configs/example.json \
  --kols "https://linkedin.com/in/kol1,https://linkedin.com/in/kol2"

Test mode:

python3 skills/kol-engager-icp/scripts/kol_engager_icp.py \
  --config skills/kol-engager-icp/configs/example.json --test