# Referral

> Referral systems - referral programs, viral loops. Use for referrals.

- Skill: `majiayu000/referral` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/referral`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/referral/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/referral

---


# Referral Guideline

## Tech Stack

* **Analytics**: PostHog
* **Database**: Neon (Postgres)

## Non-Negotiables

* Referral rewards must have clawback capability for fraud
* Attribution must be auditable (who referred whom, when, reward status)
* Velocity controls must exist to prevent abuse

## Context

Referral programs can drive explosive growth — or become fraud magnets. The best referral programs make sharing natural and rewarding. The worst become liability when abusers exploit them.

Consider both sides: what makes users want to share? And what prevents bad actors from gaming the system? A referral program that's easy to abuse is worse than no referral program.

## Driving Questions

* Why would a user share this product with someone they know?
* How easy is it for a bad actor to generate fake referrals?
* What fraud patterns exist that we haven't addressed?
* What is the actual ROI of the referral program?
* Where do users drop off in the referral/share flow?
* If we redesigned referrals from scratch, what would be different?