Kol Engager Icp Agent Skill

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

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

KOL Input

• 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 — harvestapi/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)
• harvestapi/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