kol-engager-icp
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.
npx skills add gooseworks-ai/goose-skills --skill kol-engager-icp --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```bash skills/kol-engager-icp/configs/{client-name}.json ``` Config JSON structure: ```json { "client_name": "example", "topic_keywords": ["freight automation", "dispatch operations"], "topic_patterns": ["freight.*automat", "dispatch.*oper"], "icp_k
- Phase 0: Intake
- ICP Criteria
- KOL Input
- Phase 1: Run the Pipeline
- Pipeline Steps
- Hard Caps
- Probe Mode
- Phase 2: Review & Refine
- Phase 3: Output
- Tools Required
- Example Usage
python3 skills/kol-engager-icp/scripts/kol_engager_icp.py \ Use KOL URLs from discovery output
What does the kol-engager-icp skill do?
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.
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill kol-engager-icp --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.