What makes Referral Engine notable?

1K downloads. Trust evidence available

How should Referral Engine be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Referral Engine

Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a...

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.1.0release · observed Jun 5, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17974h9acjg4h7h5djv1hg51d83hcca:referral-engine

Install using `clawhub skill install s17974h9acjg4h7h5djv1hg51d83hcca:referral-engine` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/leooooooow/referral-engine before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-leooooooow-referral-engine/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

32,879 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: Referral Engine
description: Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel.
---

# Referral Engine

Design a customer referral program with incentive structures, sharing mechanics, fraud prevention rules, and tracking setup that turns existing buyers into a scalable acquisition channel. Referral is consistently the highest-converting acquisition source for ecommerce — referred customers convert at 3–5× the rate of cold traffic and have 16–25% higher LTV — but most referral programs fail because the incentive is wrong, the timing is off, or the mechanics are too complex for buyers to act on.

## Quick Reference

| Decision | Strong | Acceptable | Weak |
|---|---|---|---|
| Incentive type | Double-sided reward (both referrer and referee get value) | One-sided reward for referrer only | Discount-only incentive with no novelty |
| Trigger timing | First positive experience moment (post-delivery Day 7–14) | Post-purchase confirmation page | Sent only to all customers at once via blast email |
| Reward value | 10–20% of AOV or product credit with real perceived value | Flat $5–$10 credit | $1–$2 credit that feels insulting |
| Sharing mechanics | 1-click share with pre-filled message to WhatsApp, SMS, email | Copy-paste link only | Manual "tell a friend" with no tracking |
| Fraud prevention | Email domain checks, IP/device deduplication, minimum order before payout | Basic duplicate email check | No fraud protection |
| Program measurement | Track referral CAC vs. other channels; CLV of referred cohort | Track total referrals sent | Count referral codes shared only |
| Program visibility | Persistent account page link + post-purchase flow + triggered email | Only in one email | Hidden in footer |

## Solves

- High customer acquisition cost from paid channels with no organic growth loop
- Strong product-market fit but weak word-of-mouth spread
- Loyal customers who would refer but have no easy mechanism to do so
- New store or brand with low ad budget needing cost-efficient first customers
- Existing customers who don't re-engage after their first purchase
- Discount dependency cycle — needing to offer promos to drive repeat business
- No measurable advocacy metric tied to customer satisfaction

## Workflow

### Step 1 — Define Program Economics

Before designing the referral experience, validate the economics work for your margins.

**Unit economics check:**

| Metric | Your number | Target range |
|---|---|---|
| Average Order Value (AOV) | | |
| Gross margin % | | |
| Current CAC (paid channels) | | |
| Target referral CAC | | <50% of paid CAC |
| Maximum reward budget | | <25% of gross margin on referred order |

**Reward type options by margin profile:**

| Margin | Best reward type | Why |
|---|---|---|
| >50% GM | Product credit or free item | High perceived value, low real cost |
| 30–50% GM | D

_meta.json

{
  "ownerId": "kn70fv0ehp50emedet9tx3fekd82pw3b",
  "slug": "referral-engine",
  "version": "1.1.0",
  "publishedAt": 1780670545064
}

fraud-prevention-rules.md

# Referral Fraud Prevention Rules

## Fraud Risk by Program Type

| Program type | Fraud risk | Primary attack vectors |
|---|---|---|
| Open referral (any customer) | Medium | Self-referral, fake account creation |
| Post-purchase gated | Low-Medium | Family/household self-referral |
| High-value rewards ($50+) | High | Organized fraud rings, fake account farms |
| Free product reward | High | Bulk account creation to claim free items |

## Tier 1 Controls — Implement for Every Program

### 1. Reward on shipped order, never on sign-up or add-to-cart
- Fraud attack: Create fake account → use referral code → claim reward without buying
- Control: Reward credit is issued only after referred order ships and return window opens

### 2. Self-referral email domain detection
- Fraud attack: Customer uses their own code with a second email address
- Control: If referrer email domain matches referee email domain → flag for review
- Also flag: obvious pattern variations ([email protected] refers [email protected])

### 3. Minimum order value threshold
- Set minimum: $25–$50 before referred order qualifies
- Prevents: Creating an account, ordering the cheapest item to unlock a $15 credit

### 4. Duplicate IP address detection
- If 3+ referral code uses originate from the same IP in 24 hours → auto-flag
- Legitimate use case: family member also orders (allow 2 per IP; flag at 3+)

## Tier 2 Controls — Add for High-Value Programs

### 5. Payout delay = return window
- If your return policy is 30 days, don't issue reward until Day 31
- Prevents: Order → claim referral discount → immediately return the order

### 6. Device fingerprinting
- Most referral platforms (Friendbuy, Impact.com) offer this as a paid feature
- Detects same physical device using multiple accounts
- Useful for free product programs where creating 10 fake accounts is worth effort

### 7. Temporary email domain blocklist
Common disposable email domains to block from receiving referee rewards:
- mailinator.com, guerrillamail.com, tempmail.com, throwam.com, yopmail.com, dispostable.com

### 8. Phone number verification for high-value rewards
- Require SMS verification before reward activates
- One phone number = one referee account
- Significantly reduces fake account creation

## Tier 3 Controls — For Enterprise / High-Fraud-Risk Programs

### 9. Manual review queue
- Auto-flag any order that triggers 2+ fraud signals
- Human review before reward issuance
- Target: review within 24 hours

### 10. Velocity rules
- Max referral rewards per customer per month: 5–10 (above this = abnormal)
- If referrer earns 5+ rewards in 30 days → pause account pending review
- Legitimate super-referrers are rare; high volume almost always indicates fraud

## Red Flags Requiring Investigation

| Signal | What it likely means |
|---|---|
| 10+ referral uses from same IP in one day | Fraud ring or organized abuse |
| Referee email addresses follow a pattern (user1@, user2@, user3@) | Bulk fake account creation

incentive-calculator.md

# Referral Incentive Calculator

## Unit Economics Worksheet

| Input | Your value |
|---|---|
| Average Order Value (AOV) | $ |
| Gross Margin % | % |
| Gross Margin per order | $ (AOV × GM%) |
| Current CAC (paid channels) | $ |
| Target referral CAC | $ (aim for 40–60% of paid CAC) |
| Maximum reward budget per acquisition | $ (GM per order − target referral CAC) |

## Reward Value by Margin Tier

### High-margin brands (>55% GM)
- Referrer: $15–20 store credit or free product ($12–18 cost)
- Referee: 15–20% off first order
- Total reward cost per acquisition: ~$20–35
- Viable if: AOV > $60 and referral CAC < $35

### Mid-margin brands (35–55% GM)
- Referrer: $10–15 store credit or 15% off next order
- Referee: 10–15% off first order
- Total reward cost per acquisition: ~$15–25
- Viable if: AOV > $45 and referral CAC < $25

### Lower-margin brands (<35% GM)
- Referrer: 10% off next order (cost deferred to second purchase)
- Referee: 10% off first order
- Alternative: Unlock reward only after referred customer makes second purchase
- Note: Straightforward cash rewards may not be viable at this margin level

## Reward Type Comparison

| Reward type | Perceived value | Actual cost | Share rate impact | Best for |
|---|---|---|---|---|
| Free product (travel/sample size) | High | Low (COGS) | +30–40% vs. cash | Brands with low-COGS samples |
| Store credit | High | Medium (discounts future revenue) | Neutral | Subscription or repeat-purchase brands |
| % discount off next order | Medium | Medium | Neutral | Mid-margin brands |
| Flat $ cash discount | Medium | Medium | Baseline | Any brand |
| Exclusive early access | High | Very low | +15–25% | Brands with strong product pipeline |
| Charity donation on their behalf | Low-medium | Low | Below baseline | Only for brand-values-driven audiences |

## Double-Sided vs. Single-Sided

Research consistently shows double-sided referral programs (both referrer and referee get rewards) outperform single-sided by 30–50% in:
- Share rate (referrer more willing to share when friend also benefits)
- Referee conversion rate (new customer more likely to act on offer)
- Referral program NPS (customers view program as generous, not transactional)

**Minimum viable double-sided offer:**
- Referrer: 10% off next order or $10 credit
- Referee: 10% off first order
- Combined cost at 40% GM on $50 AOV: ~$10 per acquisition (vs. paid CAC of $35+)

## Break-Even Referral Rate Calculator

How many referrals must convert to justify program costs?

| Fixed program cost (software) | $99/month (e.g., Smile.io Pro) |
|---|---|
| Variable cost per referred acquisition | $18 average reward cost |
| Break-even referrals to cover software | 99 ÷ 18 = 6 referral conversions to cover tool cost |

At 6+ referral acquisitions per month, even basic referral tools pay for themselves before any CAC comparison.

output-template.md

# Referral Program Design Brief

## Brand Overview
- **Brand name:** 
- **AOV:** $
- **Gross margin %:** %
- **Current paid CAC:** $
- **Target referral CAC:** $

## Program Economics
- **Referrer reward:** 
- **Referee reward:** 
- **Minimum order to activate reward:** $
- **Reward payout delay:** ___ days after delivery
- **Estimated reward cost per acquisition:** $

## Program Structure
- **Type:** Standard / Loyalty-gated / Ambassador / Group mechanic
- **Eligibility:** All customers / Repeat buyers / Loyalty tier members

## Share Experience
- **Referral link format:** 
- **Pre-written share message:**

> [Draft message here — 2–3 sentences, first person, names the offer]

- **Share channels enabled:** WhatsApp / SMS / Email / Copy link / Facebook / Instagram
- **Visual referral card:** Yes / No (design needed)

## Integration
- **Platform:** Shopify / WooCommerce / Custom
- **Referral tool:** Smile.io / ReferralCandy / Yotpo / Friendbuy / Custom
- **Email trigger:** Post-purchase Day ___ / Order confirmation / My Account

## Fraud Prevention
- [ ] Reward activates on shipped order only
- [ ] Self-referral email domain check enabled
- [ ] IP deduplication enabled
- [ ] Minimum order threshold: $___
- [ ] Payout delay: ___ days
- [ ] Device fingerprinting: Yes / No
- [ ] Temp email domain blocklist: Yes / No

## Sharing Touchpoints
- [ ] Order confirmation page
- [ ] Day 7–14 post-delivery email
- [ ] My Account → Referrals page (persistent)
- [ ] Packaging insert / unboxing card

## Launch Plan
- **Soft launch cohort:** ___ top customers (target: 100–200)
- **Soft launch date:** 
- **Full launch date:** 
- **Announcement email subject A:** 
- **Announcement email subject B:** 

## Success Metrics
| Metric | 30-day target | 90-day target |
|---|---|---|
| Share rate | % | % |
| Referee conversion rate | % | % |
| New customers via referral | | |
| Referral CAC | $ | $ |
| Referral % of new customer mix | % | % |

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