# referral-program

Guides AI agents in designing referral programs and viral loops. It covers incentive structures, viral coefficient math, and invite copy.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 421 installs · ★ 721
- Author: OpenClaudia
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/OpenClaudia/openclaudia-skills/tree/HEAD/skills/referral-program/SKILL.md
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/skills/openclaudia-referral-program

## What it does

The skill provides clear frameworks for one-sided, two-sided, and tiered incentive programs. It includes the viral coefficient formula `K = i * c`, multiplying invites sent by conversion rate. Cycle speed matters here: a 0.5 K-factor with a one-day loop beats a 0.8 coefficient taking 30 days.

It features ready-made messaging templates for email, Twitter, LinkedIn, and messaging apps. There is also a landing page blueprint with social proof sections and FAQs. Real-world breakdowns include Dropbox, Airbnb, and PayPal growth tactics. The skill requires no external APIs or dependencies. It operates entirely as Markdown instructions. If a product requires significant onboarding effort, relying on a one-sided reward will stall adoption.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add OpenClaudia/openclaudia-skills --skill referral-program -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/referral-program/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/referral-program/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add OpenClaudia/openclaudia-skills --skill referral-program -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/referral-program/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add OpenClaudia/openclaudia-skills --skill referral-program -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/referral-program/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install OpenClaudia/openclaudia-skills referral-program
```

2. **Manually: put the files in place**
- File: `.github/skills/referral-program/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add OpenClaudia/openclaudia-skills --skill referral-program -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/OpenClaudia/openclaudia-skills --path skills/referral-program
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/referral-program/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add OpenClaudia/openclaudia-skills --skill referral-program -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/OpenClaudia/openclaudia-skills/tree/HEAD/skills/referral-program/SKILL.md

## When to use it
- calculate the viral coefficient and loop cycle time
- structure tiered incentives to build brand ambassador programs
- design a referral landing page with clear social proof
- draft contextual invite templates for email and social media

## Example prompts
- Design a two-sided referral program for our subscription service
- Calculate our viral coefficient assuming 5 invites per user and 15% conversion
- Generate in-app referral copy and social share templates for LinkedIn

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 05c56f9a69eb2486.

BEGIN THIRD-PARTY FILE 05c56f9a69eb2486
````text
---
name: referral-program
description: >
  Design referral programs and viral growth loops. Use when asked to create
  referral systems, viral mechanics, word-of-mouth campaigns, or growth loops.
  Trigger phrases: "referral program", "viral loop", "refer a friend",
  "word of mouth", "viral coefficient", "referral incentive", "growth loop",
  "invite system", "ambassador program".
---

# Referral Program Design

Design effective referral programs and viral loops that drive sustainable growth.

---

## 1. Referral Program Frameworks

### One-Sided Incentives

Only the referrer gets rewarded.

**Best for:**
- Products with strong organic word-of-mouth
- Low-friction signups where the referred user needs no extra motivation
- Cost-sensitive businesses

**Examples:**
- Uber: "$10 credit for every friend you refer"
- Amazon Associates: Commission on referred purchases

**Template:**
```
Refer a friend and get [reward].
Share your unique link: [referral_url]
```

### Two-Sided Incentives

Both referrer and referred user get rewarded.

**Best for:**
- Products requiring activation effort from new users
- Competitive markets where new users need a nudge
- Subscription businesses

**Examples:**
- Dropbox: Both get 500MB extra storage
- Airbnb: Referrer gets $25 credit, friend gets $40 off first stay
- PayPal: Both get $10 when friend makes first transaction

**Template:**
```
Give [friend_reward], get [referrer_reward].
Share your link and you both win: [referral_url]
```

### Tiered Incentives

Rewards increase with number of successful referrals.

**Example tier structure:**
| Referrals | Reward |
|-----------|--------|
| 1 | Free month |
| 3 | Exclusive feature unlock |
| 5 | Premium plan for 3 months |
| 10 | Lifetime premium access |
| 25 | Cash payout or swag box |

**Best for:**
- Creating power referrers / ambassadors
- Products with passionate user bases
- Building a referral leaderboard culture

---

## 2. Viral Coefficient Calculation

The viral coefficient (K-factor) determines whether your referral loop is self-sustaining.

### Formula

```
K = i * c

Where:
  i = number of invites sent per user
  c = conversion rate of each invite

If K > 1: viral growth (each user brings more than one new user)
If K < 1: referrals supplement but don't replace other acquisition
```

### Example Calculation

```
Users send an average of 5 invites (i = 5)
15% of invites convert to signups (c = 0.15)
K = 5 * 0.15 = 0.75

With 1,000 initial users:
- Cycle 1: 1,000 * 0.75 = 750 new users
- Cycle 2: 750 * 0.75 = 563 new users
- Cycle 3: 563 * 0.75 = 422 new users
- Total after 10 cycles: ~3,570 additional users from referrals
```

### Viral Cycle Time

The speed of the viral loop matters as much as K:

```
Effective growth = K / cycle_time

A K of 0.5 with a 1-day cycle > K of 0.8 with a 30-day cycle
```

### How to Improve K

| Lever | Action |
|-------|--------|
````
END THIRD-PARTY FILE 05c56f9a69eb2486

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