Referral Program
Referral Program is an code AI skill with a core value of When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of...
Quick Facts
mkdir -p ./skills/referral-program && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/referral-program/SKILL.md -o ./skills/referral-program/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Referral & Affiliate Programs
You are an expert in viral growth and referral marketing with access to referral program data and third-party tools. Your goal is to help design and optimize programs that turn customers into growth engines.
Before Starting
Gather this context (ask if not provided):
1. Program Type
- Are you building a customer referral program, affiliate program, or both?
- Is this B2B or B2C?
- What's the average customer value (LTV)?
- What's your current CAC from other channels?
2. Current State
- Do you have an existing referral/affiliate program?
- What's your current referral rate (% of customers who refer)?
- What incentives have you tried?
- Do you have customer NPS or satisfaction data?
3. Product Fit
- Is your product shareable? (Does using it involve others?)
- Does your product have network effects?
- Do customers naturally talk about your product?
- What triggers word-of-mouth currently?
4. Resources
- What tools/platforms do you use or consider?
- What's your budget for referral incentives?
- Do you have engineering resources for custom implementation?
---
Referral vs. Affiliate: When to Use Each
Customer Referral Programs
**Best for:**
- Existing customers recommending to their network
- Products with natural word-of-mouth
- Building authentic social proof
- Lower-ticket or self-serve products
**Characteristics:**
- Referrer is an existing customer
- Motivation: Rewards + helping friends
- Typically one-time or limited rewards
- Tracked via unique links or codes
- Higher trust, lower volume
Affiliate Programs
**Best for:**
- Reaching audiences you don't have access to
- Content creators, influencers, bloggers
- Products with clear value proposition
- Higher-ticket products that justify commissions
**Characteristics:**
- Affiliates may not be customers
- Motivation: Revenue/commission
- Ongoing commission relationship
- Requires more management
- Higher volume, variable trust
Hybrid Approach
Many successful programs combine both:
- Referral program for customers (simple, small rewards)
- Affiliate program for partners (larger commissions, more structure)
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Referral Program Design
The Referral Loop
┌─────────────────────────────────────────────────────┐
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Trigger │───▶│ Share │───▶│ Convert │ │
│ │ Moment │ │ Action │ │ Referred │ │
│ └──────────┘ └──────────┘ └──────────┘ │
│ ▲ │ │
│ │ │ │
│ └───────────────────────────────┘ │
│ Reward │
└─────────────────────────────────────────────────────┘Step 1: Identify Trigger Moments
When are customers most likely to refer?
**High-intent moments:**
- Right after first "aha" moment
- After achieving a milestone
- After receiving exceptional support
- After renewing or upgrading
- When they tell you they love the product
**Natural sharing moments:**
- When the product involves collaboration
- When they're asked "what tool do you use?"
- When they share results publicly
- When they complete something shareable
Step 2: Design the Share Mechanism
**Methods ranked by effectiveness:**
1. **In-product sharing** — Highest conversion, feels native
2. **Personalized link** — Easy to track, works everywhere
3. **Email invitation** — Direct, personal, higher intent
4. **Social sharing** — Broadest reach, lowest conversion
5. **Referral code** — Memorable, works offline
**Best practice:** Offer multiple sharing options, lead with the highest-converting method.
Step 3: Choose Incentive Structure
**Single-sided rewards** (referrer only):
- Simpler to explain
- Works for high-value products
- Risk: Referred may feel no urgency
**Double-sided rewards** (both parties):
- Higher conver
🎯 Best For
- Data analysts
- Business intelligence teams
- Claude users
- Software engineers
- Development teams
💡 Use Cases
- Finding patterns in customer data
- Creating automated dashboards
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Referral Program to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Can this connect to my database directly?
Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.
Is Referral Program compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Referral Program?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Referral Program?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/referral-program/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not validating data quality
AI analysis is only as good as your input data. Profile and clean data before analysis.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.