levalencia Champion Tracker
Detect when product champions change jobs and qualify their new companies against ICP.
When to Use
- You have a list of known product users/champions (from reviews, LinkedIn posts, CRM exports)
- You want to detect when they change companies (high-intent re-sell signal)
- You want each job change scored against ICP before reaching out
Two Phases
Phase A: Discover Champions (agent-driven, one-time)
Build the initial champion list from public sources. This is done by the agent, not the script.
- Scrape reviews — Use
review-scraperskill to pull G2/Trustpilot reviews. Extract reviewer names + companies. - Search LinkedIn posts — Use Crustdata MCP to find people who posted about the product.
- Resolve LinkedIn URLs — Use Crustdata MCP to search by name + company → get profile URLs.
- Compile CSV — Merge all sources into
champions.csvwith required columns.
Phase B: Track Job Changes (script-driven, repeatable)
Use champion_tracker.py for ongoing tracking.
Script Usage
Prerequisites
APIFY_API_TOKENin.env(for LinkedIn profile enrichment)- Champion CSV with columns:
name,linkedin_url(required);original_company,original_title,email,source,notes(optional)
Commands
Initialize baseline (first run):
# Dry run — see cost estimate
python3 skills/champion-tracker/scripts/champion_tracker.py init -i champions.csv --dry-run
# Create baseline
python3 skills/champion-tracker/scripts/champion_tracker.py init -i champions.csv
Check for job changes (subsequent runs):
# Dry run
python3 skills/champion-tracker/scripts/champion_tracker.py check --dry-run
# Detect changes and output CSV
python3 skills/champion-tracker/scripts/champion_tracker.py check -o changes.csv
View status:
python3 skills/champion-tracker/scripts/champion_tracker.py status
Output CSV Columns
| Column | Description |
|---|---|
| champion_name | Full name |
| linkedin_url | LinkedIn profile URL |
| previous_company | Company at baseline |
| previous_title | Title at baseline |
| new_company | Current company (changed) |
| new_title | Current title |
| change_detected_date | Date this check was run |
| position_start_date | When they started the new role |
| days_since_change | Days since new position started |
| icp_score | 0-4 ICP qualification score |
| icp_verdict | Strong Fit / Good Fit / Possible Fit / Weak Fit |
| icp_notes | Scoring breakdown |
| Email if available | |
| notes | Original notes from champion CSV |
ICP Scoring (0-4)
| Signal | Points | What it checks |
|---|---|---|
| B2B signal | 1.0 | Title contains sales/SDR/revenue/growth keywords |
| Outbound motion | 1.0 | Sales leadership title (VP Sales, Head of Growth, etc.) |
| Company size | 1.0 / 0.5 | SMB/mid-market = 1.0; unknown = 0.5 benefit-of-doubt |
| Seniority | 1.0 | VP, Director, Head of, C-level, Founder |
Verdicts: Strong Fit (>=3) / Good Fit (>=2) / Possible Fit (>=1.5) / Weak Fit (<1.5)
Cost
- ~$3 per 1,000 LinkedIn profiles enriched
- 50-80 champions ≈ $0.15-0.25 per run
--dry-runalways shows cost before any API calls
File Structure
skills/champion-tracker/
SKILL.md # This file
scripts/
champion_tracker.py # Main CLI script
input/
champions_template.csv # Template for manual additions
snapshots/ # Created at runtime
baseline.json # Latest full snapshot
archive/ # Timestamped copies
output/ # Created at runtime
changes-YYYY-MM-DD.csv # Generated output
Dependencies
- Reuses
LinkedInEnricherfromskills/lead-qualification/scripts/enrich_leads.py - Falls back to inline implementation if import fails
- Requires:
requests(Python package),APIFY_API_TOKEN(env var)