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referral
by SylphxAI
🚀 AI development platform with MEP architecture - stop writing prompts, start building with 90% less typing
⭐ 4🍴 3📅 Jan 8, 2026
SKILL.md
name: referral description: Referral systems - referral programs, viral loops. Use for referrals.
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?
Score
Total Score
75/100
Based on repository quality metrics
✓SKILL.md
SKILL.mdファイルが含まれている
+20
✓LICENSE
ライセンスが設定されている
+10
✓説明文
100文字以上の説明がある
+10
○人気
GitHub Stars 100以上
0/15
✓最近の活動
3ヶ月以内に更新
+5
○フォーク
10回以上フォークされている
0/5
✓Issue管理
オープンIssueが50未満
+5
✓言語
プログラミング言語が設定されている
+5
✓タグ
1つ以上のタグが設定されている
+5
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