Champion Tracker Agent Skill
> 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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/gooseworks-ai/goose-skills --skill champion-trackerWhat comes with it
36 532 bytes besides the instructionThe instruction itself
13 sections, as written by the authorChampion 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-site-scraperskill to pull G2/Trustpilot reviews. Extract reviewer names + companies. - Search LinkedIn posts — Use the
linkedin-post-researchskill (Apify-based) to find people who posted about the product. - Resolve LinkedIn URLs — Use Fiber
/v1/kitchen-sink/person(name + company → profile URL) or ContactOut via Orthogonal. - 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 | 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)
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How to use it
Take gooseworks-ai/champion-tracker from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.