/kol-engager-icp

name: kol-engager-icp
description: >
  Find ICP-fit leads from KOL audiences on LinkedIn. Given a list of KOLs,
  scrapes their most relevant high-engagement post from the last 30 days,
  extracts engagers (reactors + commenters), pre-filters by position,
  enriches top profiles, and ICP-classifies. Cost-controlled: 1 post per KOL.
  Use when someone wants to "find leads from KOL audiences" or "scrape
  engagers from influencer posts" or after running kol-discovery.
tags: [lead-generation]

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** — `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)

E