lead-generation

Champion Tracker

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.

by Athina AI

Run in Gooseworks View skill in Github

Install

Terminal

npx gooseworks install --all

# then, in Claude Code, Cursor, or Codex:
/gooseworks use the champion-tracker skill

About This Skill

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-site-scraper skill to pull G2/Trustpilot reviews. Extract reviewer names + companies.
Search LinkedIn posts — Use the linkedin-post-research skill (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.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)

What's included

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

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)

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Transcript Intelligence

Turn TikTok, Instagram, YouTube, Facebook, X, LinkedIn, Reddit, or Rumble transcripts into timestamped hooks, claims, objections, proof, calls to action, sponsorship signals, and reusable content atoms. Use directly for transcript analysis or as support for creator, competitor, trend, demand, and repurposing work.

Social Listening Brief

Produce a decision-ready brief of current brand, product, category, and competitor conversations across social platforms, including sentiment drivers, questions, risks, and growth opportunities.

Champion Tracker

Champion Tracker

When to Use

Two Phases

Phase A: Discover Champions (agent-driven, one-time)

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

Script Usage

Prerequisites

Commands

Output CSV Columns

ICP Scoring (0-4)

Cost

File Structure

Dependencies

What's included

Trend Discovery

Transcript Intelligence

Social Listening Brief

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