champion-tracker

name	champion-tracker
description	Track product champions for job changes and qualify their new companies against ICP. Takes a CSV of known champions (with LinkedIn URLs), creates a baseline snapshot via Apify enrichment, then detects when champions move to new companies. Scores new companies on a 0-4 ICP fit scale. Outputs a downloadable CSV of movers with qualification verdicts.
tags	["lead-generation"]

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.

1. Scrape reviews — Use review-scraper skill to pull G2/Trustpilot reviews. Extract reviewer names + companies.
2. Search LinkedIn posts — Use Crustdata MCP to find people who posted about the product.
3. Resolve LinkedIn URLs — Use Crustdata MCP to search by name + company → get profile URLs.
4. Compile CSV — Merge all sources into champions.csv with required columns.

Phase B: Track Job Changes (script-driven, repeatable)

Use champion_tracker.py for ongoing tracking.

Script Usage

Prerequisites

• APIFY_API_TOKEN in .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	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-run always 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 LinkedInEnricher from skills/lead-qualification/scripts/enrich_leads.py
• Falls back to inline implementation if import fails
• Requires: requests (Python package), APIFY_API_TOKEN (env var)