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claude-code

Claude-skill-registry fraud-detection

Use to monitor, investigate, and prevent abuse within referral programs.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/fraud-detection" ~/.claude/skills/majiayu000-claude-skill-registry-fraud-detection && rm -rf "$T"
manifest: skills/data/fraud-detection/SKILL.md
source content

Referral Fraud Detection Skill

When to Use

  • Designing safeguards for new referral initiatives.
  • Investigating suspicious referral spikes, duplicate accounts, or payout anomalies.
  • Reporting on program integrity for finance, legal, or compliance teams.

Framework

  1. Signal Collection – IP/device matching, velocity checks, blacklist databases, manual reviews.
  2. Scoring Model – assign risk scores by cohort (new accounts, high-volume referrers, geo mismatch).
  3. Workflow Automation – auto-flag, queue for review, or pause rewards until verified.
  4. Investigation Runbook – define evidence gathering, communication templates, and resolution paths.
  5. Feedback Loop – update heuristics, adjust incentives, and communicate policy changes.

Templates

  • Fraud monitoring dashboard outline (metrics, thresholds, owners).
  • Investigation log (case ID, referrer, signals, action taken, notes).
  • Policy update checklist (legal, comms, ops, partner notifications).

Tips

  • Combine automated checks with random manual audits for accuracy.
  • Align with legal/finance on clawback procedures before launch.
  • Share learnings with 
    incentive-design
    to discourage risky behavior.