referral-program

coreyhaines31/marketingskills · updated Apr 8, 2026

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$npx skills add https://github.com/coreyhaines31/marketingskills --skill referral-program
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summary

Design and optimize customer referral and affiliate programs to turn users into growth engines.

  • Covers both customer referral programs (existing users recommending to networks) and affiliate programs (ongoing commission relationships with creators and partners)
  • Provides step-by-step referral loop design: trigger moments, share mechanisms, incentive structures (single-sided, double-sided, tiered), and conversion optimization
  • Includes A/B testing frameworks, fraud prevention, and measu
skill.md

Referral & Affiliate Programs

You are an expert in viral growth and referral marketing. Your goal is to help design and optimize programs that turn customers into growth engines.

Before Starting

Check for product marketing context first: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Program Type

  • Customer referral program, affiliate program, or both?
  • B2B or B2C?
  • What's the average customer LTV?
  • What's your current CAC from other channels?

2. Current State

  • Existing referral/affiliate program?
  • Current referral rate (% who refer)?
  • What incentives have you tried?

3. Product Fit

  • Is your product shareable?
  • Does it have network effects?
  • Do customers naturally talk about it?

4. Resources

  • Tools/platforms you use or consider?
  • Budget for referral incentives?

Referral vs. Affiliate

Customer Referral Programs

Best for:

  • Existing customers recommending to their network
  • Products with natural word-of-mouth
  • Lower-ticket or self-serve products

Characteristics:

  • Referrer is an existing customer
  • One-time or limited rewards
  • Higher trust, lower volume

Affiliate Programs

Best for:

  • Reaching audiences you don't have access to
  • Content creators, influencers, bloggers
  • Higher-ticket products that justify commissions

Characteristics:

  • Affiliates may not be customers
  • Ongoing commission relationship
  • Higher volume, variable trust

Referral Program Design

The Referral Loop

Trigger Moment → Share Action → Convert Referred → Reward → (Loop)

Step 1: Identify Trigger Moments

High-intent moments:

  • Right after first "aha" moment
  • After achieving a milestone
  • After exceptional support
  • After renewing or upgrading

Step 2: Design Share Mechanism

Ranked by effectiveness:

  1. In-product sharing (highest conversion)
  2. Personalized link
  3. Email invitation
  4. Social sharing
  5. Referral code (works offline)

Step 3: Choose Incentive Structure

Single-sided rewards (referrer only): Simpler, works for high-value products

Double-sided rewards (both parties): Higher conversion, win-win framing

Tiered rewards: Gamifies referral process, increases engagement

For examples and incentive sizing: See references/program-examples.md


Program Optimization

Improving Referral Rate

If few customers are referring:

  • Ask at better moments
  • Simplify sharing process
  • Test different incentive types
  • Make referral prominent in product

If referrals aren't converting:

  • Improve landing experience for referred users
  • Strengthen incentive for new users
  • Ensure referrer's endorsement is visible

A/B Tests to Run

Incentive tests: Amount, type, single vs. double-sided, timing

Messaging tests: Program description, CTA copy, landing page copy

Placement tests: Where and when the referral prompt appears

Common Problems & Fixes

Problem Fix
Low awareness Add prominent in-app prompts
Low share rate Simplify to one click
Low conversion Optimize referred user experience
Fraud/abuse Add verification, limits
One-time referrers Add tiered/gamified rewards

Measuring Success

Key Metrics

Program health:

  • Active referrers (referred someone in last 30 days)
  • Referral conversion rate
  • Rewards earned/paid

Business impact:

  • % of new customers from referrals
  • CAC via referral vs. other channels
  • LTV of referred customers
  • Referral program ROI

Typical Findings

  • Referred customers have 16-25% higher LTV
  • Referred customers have 18-37% lower churn
  • Referred customers refer others at 2-3x rate

Launch Checklist

Before Launch

  • Define program goals and success metrics
  • Design incentive structure
  • Build or configure referral tool
  • Create referral landing page
  • Set up tracking and attribution
  • Define fraud prevention rules
  • Create terms and conditions
  • Test complete referral flow

Launch

  • Announce to existing customers
  • Add in-app referral prompts
  • Update website with program details
  • Brief support team

Post-Launch (First 30 Days)

  • Review conversion funnel
  • Identify top referrers
  • Gather feedback
  • Fix friction points
  • Send reminder emails to non-referrers

Email Sequences

Referral Program Launch

Subject: You can now earn [reward] for sharing [Product]

We just launched our referral program!

Share [Product] with friends and earn [reward] for each signup.
They get [their reward] too.

[Unique referral link]

1. Share your link
2. Friend signs up
3. You both get [reward]

Referral Nurture Sequence

  • Day 7: Remind about referral program
  • Day 30: "Know anyone who'd benefit?"
  • Day 60: Success story + referral prompt
  • After milestone: "You achieved [X]—know others who'd want this?"

Affiliate Programs

For detailed affiliate program design, commission structures, recruitment, and tools: See references/affiliate-programs.md


Task-Specific Questions

  1. What type of program (referral, affiliate, or both)?
  2. What's your customer LTV and current CAC?
  3. Existing program or starting from scratch?
  4. What tools/platforms are you considering?
  5. What's your budget for rewards/commissions?
  6. Is your product naturally shareable?

Tool Integrations

For implementation, see the tools registry. Key tools for referral programs:

Tool Best For Guide
Rewardful Stripe-native affiliate programs rewardful.md
Tolt SaaS affiliate programs tolt.md
Mention Me Enterprise referral programs mention-me.md
Dub.co Link tracking and attribution dub-co.md
Stripe Payment processing (for commission tracking) stripe.md
Introw Channel partner programs with tiers, deal registration, QBRs introw.md
PartnerStack Enterprise partner and affiliate programs partnerstack.md

Related Skills

  • launch-strategy: For launching referral program effectively
  • email-sequence: For referral nurture campaigns
  • marketing-psychology: For understanding referral motivation
  • analytics-tracking: For tracking referral attribution
how to use referral-program

How to use referral-program on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add referral-program
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/coreyhaines31/marketingskills --skill referral-program

The skills CLI fetches referral-program from GitHub repository coreyhaines31/marketingskills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/referral-program

Reload or restart Cursor to activate referral-program. Access the skill through slash commands (e.g., /referral-program) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.771 reviews
  • Chaitanya Patil· Dec 28, 2024

    I recommend referral-program for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kofi Ghosh· Dec 28, 2024

    referral-program fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kofi Gupta· Dec 28, 2024

    referral-program has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aarav Garcia· Dec 24, 2024

    referral-program fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Advait Martin· Dec 20, 2024

    referral-program is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Yuki Dixit· Dec 20, 2024

    I recommend referral-program for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Nikhil Mensah· Dec 16, 2024

    Keeps context tight: referral-program is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Anaya Rao· Nov 27, 2024

    Registry listing for referral-program matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Piyush G· Nov 19, 2024

    Useful defaults in referral-program — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Amina Torres· Nov 19, 2024

    We added referral-program from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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