Churn Prevention
freekmurze/dotfiles
When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies.
A skill your agent uses when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and…
Install 18 Referral Program Global
$ npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install minhnv0807/ai-business-skills 18-referral-program-global --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/en/18-referral-program-global .claude/skills/18-referral-program-global && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "18-referral-program-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global into .claude/skills/18-referral-program-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "18-referral-program-global", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-globalType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install minhnv0807/ai-business-skills 18-referral-program-global --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/en/18-referral-program-global .agents/skills/18-referral-program-global && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "18-referral-program-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global into .agents/skills/18-referral-program-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "18-referral-program-global", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install minhnv0807/ai-business-skills 18-referral-program-global --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/en/18-referral-program-global .cursor/skills/18-referral-program-global && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "18-referral-program-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global into .cursor/skills/18-referral-program-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "18-referral-program-global", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/minhnv0807/ai-business-skills.git --path skills/en/18-referral-program-global--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install minhnv0807/ai-business-skills 18-referral-program-global --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/en/18-referral-program-global .gemini/skills/18-referral-program-global && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "18-referral-program-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global into .gemini/skills/18-referral-program-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "18-referral-program-global", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install minhnv0807/ai-business-skills 18-referral-program-globalInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/en/18-referral-program-global .github/skills/18-referral-program-global && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "18-referral-program-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global into .github/skills/18-referral-program-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "18-referral-program-global", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install minhnv0807/ai-business-skills 18-referral-program-global --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/en/18-referral-program-global .opencode/skills/18-referral-program-global && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "18-referral-program-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/18-referral-program-global into .opencode/skills/18-referral-program-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "18-referral-program-global", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
18-referral-program-global- GitHub stars
- 610
- Token cost
- ~3.8k tokens
- SKILL.md length
- 1,255 words
- Files
- 5
- Skills in repo
- 142
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and…
- Works in 9 steps: Check global context → Pick region variant → Prerequisites — does referral make sense? → …
- Customers should bring other customers — one-way versus two-way incentives
- SKILL.md covers For newbies, Why do you need this skill?, Workflow and Step 0: Check global context, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
18 Referral Program Global is an agent skill from minhnv0807/ai-business-skills. Use when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and attribution, fraud controls, and anti-spam compliance by region: TCPA for US, GDPR for EU, PDPA for SEA, LGPD for LATAM. Trigger on 'referral program', 'refer a friend', 'word of mouth', 'viral loop', 'how do I get customers to bring friends', 'affiliate rewards'. Also use when the user has happy customers and no system to use them. Not for —…
Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `variants/01-us.md`, `variants/02-eu.md` and `variants/03-sea.md`).
It sits in Marketing & SEO, covering Referral and retention marketing, Privacy and GDPR and Email marketing. The repository describes itself as: 138 bilingual AI marketing skills (69 VN + 69 Global) for Claude Code, OpenCode, Codex, VS Code. Four role SOP packs — content, design, performance, leader ops — plus strategy… The licence is MIT.
When your agent uses it
- Customers should bring other customers — one-way versus two-way incentives
- Reward structure and economics
- Trigger moments
- Referral messaging
Example prompts
- “referral program”
- “refer a friend”
- “word of mouth”
- “/18-referral-program-global”
Workflow steps
9 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 0360adc. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
18 Referral Program Global loads about 3.8k tokens when it runs. Until then it costs about 185 tokens; SKILL.md has 1,255 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from minhnv0807/ai-business-skills at commit 0360adc, republished under its MIT licence (© minhnv0807). 1,255 words, ~3,765 tokens.
Download SKILL.mdSave it as
.claude/skills/18-referral-program-global/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- 18-referral-program-global
- description
- Use when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and attribution, fraud controls, and anti-spam compliance by region: TCPA for US, GDPR for EU, PDPA for SEA, LGPD for LATAM. Trigger on 'referral program', 'refer a friend', 'word of mouth', 'viral loop', 'how do I get customers to bring friends', 'affiliate rewards'. Also use when the user has happy customers and no system to use them. Not for — running your own community space, see `28-community-building-global`; posting inside third-party communities, see `38-community-seeding-global`; email flow mechanics, see `14-email-marketing-global`.
- metadata.version
- 1.0.1
- metadata.category
- operations
- license
- MIT
- triggers
- referral program, refer a friend, word of mouth, viral loop, referral marketing
- related
- product-marketing-context-global, 14-email-marketing-global, 27-personal-brand-monetize-global, references/global-legal-compliance
Referral Program (Global)
Word of mouth is the highest-LTV acquisition channel in every region. But the LEGAL framework around how you contact referred prospects differs HUGELY: TCPA (US, SMS), GDPR (EU, all channels), PDPA (SEA), LGPD (LATAM). Pick the right region variant or get fined.
For newbies
Who is this skill for?
| Audience | Concrete example |
|---|---|
| DTC brand wanting cheaper acquisition | Already at USD 30 CAC; want USD 10 CAC via referral |
| SaaS adding viral loop | Existing PMF; want negative CAC growth |
| Service business (coaching, agency) | High-LTV; want client referrals |
| Subscription brand | High retention; turn customers into ambassadors |
| E-commerce wanting AOV growth | Refer a friend = both get discount |
Who is this NOT for?
- Vietnam-only referral -> Use
18-referral-program(VN skill) — Zalo / Messenger optimized - B2B enterprise sales -> ABM / partnership programs are different motion (not covered here)
- Brand ambassador / affiliate -> Use
27-personal-brand-monetize-globalfor influencer-affiliate (when available)
30-second pre-read
This skill produces ONE referral program design with 6 components: model selection (1-way / 2-way / multi-tier), incentive math (% of LTV), tracking infrastructure, anti-fraud measures, launch sequence, and KPIs (K-factor, viral coefficient). Pick 1 of 4 region variants — the variant tunes the LEGAL rules for contacting referred prospects (especially via SMS/email).
3 common errors
- SMS-based referral in US without TCPA consent -> Up to USD 1,500 per text fines + class actions
- Email-blast referred contacts in EU -> GDPR violation; referral programs touching EU prospects need explicit consent from the prospect, NOT just the referrer
- Cash incentives that violate FTC endorsement rules -> "Refer a friend, get USD 100" requires disclosed material connection if referrer posts publicly
Why do you need this skill?
Without proper referral design:
- US: Risk TCPA class action (USD 500-1,500 per message)
- EU: GDPR violation if you store referred-prospect data without their consent
- SEA: PDPA Singapore strict — most referral programs need both-side consent
- LATAM: Brazil LGPD treats referred contacts as data subjects requiring consent
- Universal: incentive math wrong -> losing money instead of growing
- Universal: no anti-fraud -> 30-50% of "referrals" are self-referrals or bots
Plan the legal foundation correctly, get the incentive math right, ship a working viral loop.
Workflow
Step 0: Check global context file
|-- exists -> read product / customer / region
|-- missing -> suggest user run product-marketing-context-global first
Step 1: Pick region variant (US / EU / SEA / LATAM)
Step 2: Confirm prerequisites (NPS, AOV, LTV, customer base)
Step 3: Choose model (1-way / 2-way / multi-tier affiliate)
Step 4: Calculate incentive (15-25% of LTV)
Step 5: Set up tracking + anti-fraud
Step 6: Design referral flow (7 steps)
Step 7: Launch sequence (30-day plan)
Step 8: Measure K-factor / viral coefficientStep 0: Check global context
Check .agents/product-marketing-context-global.md:
- Yes -> Read product, customer, region. Do NOT re-ask.
- No -> Suggest running
product-marketing-context-globalfirst.
Step 1: Pick region variant
Ask: "Which is your PRIMARY region: US, EU, SEA, or LATAM?"
Where do most of your customers (and their referrals) live?
|-- US / Canada --> 01-us.md (TCPA SMS rules; CAN-SPAM email; CCPA data)
|-- EU / EEA / UK --> 02-eu.md (GDPR consent for ALL channels)
|-- Southeast Asia --> 03-sea.md (PDPA per country; mostly opt-in)
|-- Latin America --> 04-latam.md (LGPD Brazil; LFPDPPP Mexico)
|-- Vietnam only --> Use `18-referral-program` (VN skill)Step 2: Prerequisites — does referral make sense?
When referral works
- NPS >= 40 (customers actively like you)
- Customer has natural reason to share (visible result, social currency, peer-relevant)
- AOV high enough to fund meaningful incentive (USD 50+ ideal)
- LTV high enough to justify CAC investment
- Existing base of 100+ happy customers to seed
When referral does NOT work (skip this skill)
- NPS < 20 (customers don't like you yet — fix retention first)
- Sensitive product category (financial advice, intimate health) — referrals feel weird
- Very low AOV (< USD 10) — incentive economics don't work
- Pre-launch or no customer base — no one to refer
Ask the user
- Product type? (DTC / SaaS / Service / Subscription)
- Average AOV and LTV?
- Existing happy customer count?
- Goal: more new customers, lower CAC, or higher engagement?
Step 3: Referral models
Model 1: One-way (referrer gets reward, referee gets nothing)
When: Premium product where referee will buy regardless of incentive Examples:
- Tesla referral program (referrer gets credit, new buyer pays full price)
- Robinhood (referrer gets free stock; referee just signs up)
Pros: Lower cost Cons: Lower conversion (referee has no extra reason to buy now)
Model 2: Two-way (BOTH referrer and referee get rewards) — DEFAULT CHOICE
When: 80% of cases; psychological "win-win" feels generous to referrer Examples:
- Airbnb (both get USD 25-50 credit)
- Uber (both get USD 5-15 credit)
- Dropbox (both get +500MB)
Pros: Higher conversion; referrer feels good giving "gift" Cons: Higher cost per acquisition
Standard 2-way structure:
Referrer gets: Discount / credit / free product / cash / reward
Referee gets: Discount / free trial / bonus on first orderModel 3: Multi-tier affiliate (% commission on revenue)
When: SaaS, high-ticket courses, premium DTC; want power-users / influencers Examples:
- ConvertKit / Kit (30% recurring affiliate)
- Shopify (200% of monthly fee per signup)
- AWeber, Teachable, Coursera (10-50% per sale)
Pros: Attracts professional affiliates / influencers; scalable Cons: Requires legal disclosures (FTC US), tracking infrastructure (Rewardful, FirstPromoter), tax forms (W-9 in US, equivalents elsewhere)
Standard tier structure:
- Tier 1: 10-30% commission on first purchase
- Tier 2: 5-15% on recurring (next 90 days or lifetime)
- Top tier: 30-50% for super-affiliates (negotiated)
Step 4: Incentive math (CRITICAL)
The formula
Total incentive (both sides combined) <= 15-25% of customer LTVWorked example (Saas)
Product: Project management SaaS
Pricing: USD 49/month
Average tenure: 18 months
LTV: USD 882 (49 x 18)
Incentive cap: 15-25% of LTV = USD 130-220 total
Two-way structure:
Referrer: 1 month free (USD 49 value) + USD 30 credit = USD 79 cost
Referee: 50% off first 2 months = USD 49 cost
Total: USD 128 (within cap)
Or simpler:
Both get 1 month free = USD 98 total cost
ROI: USD 882 LTV - USD 98 incentive = USD 784 net per successful referralWorked example (DTC)
Product: Skincare subscription
AOV: USD 50 / box
Average orders: 10
LTV: USD 500
Incentive cap: USD 75-125 total
Two-way structure:
Referrer: USD 30 credit (next box)
Referee: USD 20 off first box
Total: USD 50 (well within cap)Reward formats — pros and cons
| Format | Pros | Cons | Best for |
|---|---|---|---|
| Cash | Highest motivation | Highest cost (out of pocket) | Affiliate, B2B |
| Account credit | Keeps customer | Useless if customer leaves | Subscription, marketplace |
| Discount on next purchase | Pay-on-purchase | Customer may not return | E-commerce |
| Free product / service | Higher perceived value | Logistics complexity | Service, beauty, F&B |
| Physical gift | Tangible delight | Operational burden | Premium DTC |
| Points / rewards | Habit-forming | Requires loyalty system | Retailers, airlines |
Show full SKILL.md (471 more words)Show less
Step 5: Tracking + anti-fraud
Tracking tools (region-agnostic)
| Tool | Best for | Pricing |
|---|---|---|
| ReferralCandy | Shopify DTC | USD 49+/mo |
| Rewardful | SaaS affiliate | USD 49+/mo |
| FirstPromoter | SaaS affiliate | USD 49+/mo |
| Friendbuy | Mid-market DTC | USD 249+/mo |
| Mention Me | Premium DTC | Enterprise |
| PartnerStack | B2B SaaS partnerships | USD 500+/mo |
| Talkable | Enterprise DTC | Enterprise |
| Build in-house | Full control | Custom |
Anti-fraud measures
| Risk | Defense |
|---|---|
| Self-referral via second account | Match phone, address, payment, IP, device fingerprint |
| Public posting on coupon sites | Limit 3-5 redemptions per code; require minimum AOV |
| Bot / script signups | Captcha; rate limit; manual review for large batches |
| Cancel-after-reward | 30-day reward holding period (after return window) |
| Influencer abuse | Cap individual referrer rewards monthly; flag outliers |
| Referee buys then refunds | Hold rewards until past return window; partial reward if partial refund |
Step 6: 7-step referral flow
Step 1: Customer has good experience
|--> Trigger when NPS >= 7 OR after 2nd purchase OR completion of service
Step 2: Customer sees referral CTA
|--> Email after delivery, dashboard widget, post-purchase page, account menu
Step 3: Customer gets unique code/link
|--> Personalized: "JANE25" or unique link with UTM tracking
Step 4: Customer shares (multiple channels)
|--> Built-in share: WhatsApp, Email, SMS, Copy link, Twitter/X
|--> Pre-filled message in customer's voice
Step 5: Friend clicks / enters code
|--> Landing page tailored to referral (not generic homepage)
|--> Reward visible upfront ("Get USD 20 off")
Step 6: Friend converts (purchase)
|--> Tracking pixel fires; both parties receive notification
|--> Reward delivered automatically (or held for 30 days)
Step 7: Cycle continues
|--> Friend now eligible to refer; nudge after first delivery
|--> Top referrers get bonus tiers ("3 referrals = VIP")Step 7: 30-day launch sequence
Week 1: Setup
- Choose model (1-way / 2-way / multi-tier)
- Finalize incentive math (LTV calculation, reward structure)
- Pick tool (ReferralCandy / Rewardful / build)
- Create landing page for referee
- Set up email/SMS automation flows
- Set up tracking + attribution
- Legal review (per region variant)
Week 2: Soft launch (seed)
- Email top 50-100 happiest customers (NPS 9-10)
- Track first referrals; fix bugs
- Iterate on copy / friction points
- Verify reward delivery automation
Week 3: Public launch
- Email full customer base
- Add referral CTA to:
- Order confirmation page
- Post-delivery email
- Account dashboard
- Receipt PDF / packaging insert (offline)
- Social posts on owned channels
- Optional: paid promotion to existing customers ("Tell friends, both save")
Week 4: Optimize
- Identify top sharers (top 10%)
- Bonus push: "You're in top 10 — extra reward this month"
- A/B test:
- Landing page (referral vs. cold)
- Reward amount (USD 20 vs USD 30)
- Channel emphasis (email vs. SMS vs. WhatsApp)
Step 8: KPIs and viral coefficient
Key metrics
| Metric | Formula | Benchmark |
|---|---|---|
| Share rate | Referrers / total customers | 10% basic, 20% good, 30%+ excellent |
| Conversion rate | Successful redemptions / shares | 15% basic, 25% good, 40%+ excellent |
| Average referrals per sharer | Successful refs / sharers | 1.2 basic, 2+ good, 3+ excellent |
| K-factor (viral coefficient) | Share rate x Conversion rate x Avg refs | 0.3-0.5 typical, 1.0+ true viral |
| CAC via referral | Reward cost / referred customers | 30-50% of paid CAC |
| Referred customer LTV | Avg LTV of referred customers | Often 1.2x non-referred |
K-factor interpretation
K = 0.3 -> 100 customers bring 30 new -> sub-viral, supplements other channels
K = 0.7 -> 100 customers bring 70 new -> strong supplement
K = 1.0 -> 100 customers bring 100 new -> equilibrium (each customer replaces one)
K > 1.0 -> Viral loop! Exponential growth (rare but transformative)Most healthy referral programs target K = 0.4-0.7. K > 1 is rare and usually requires unique product mechanics (Dropbox, WhatsApp, Calendly).
Output template
# Referral Program - [Brand]
Region: [US/EU/SEA/LATAM]
Date: [YYYY-MM-DD]
## 1. Goal
[New customers / Lower CAC / Higher LTV / Multiple]
## 2. Prerequisites confirmed
- NPS: [X]
- AOV: [USD/EUR/etc.]
- LTV: [calculated]
- Customer base: [N]
## 3. Model
[1-way / 2-way / multi-tier]
## 4. Incentive structure
- Referrer gets: [reward + cost]
- Referee gets: [reward + cost]
- Total cost: [USD X, ~Y% of LTV]
## 5. Tracking tool
[ReferralCandy / Rewardful / etc.]
## 6. Anti-fraud measures
[List all 5-7 measures applied]
## 7. Referral flow (7 steps)
[Description per step]
## 8. Launch sequence (30 days)
[Week 1-4 plan]
## 9. KPIs
[Share rate, Conversion rate, K-factor target]
## 10. Legal compliance
[Per region variant — see specific variant file]Quality checklist
- Region variant chosen (US/EU/SEA/LATAM)
- NPS >= 40 confirmed (have happy customers)
- Total incentive cost <= 25% of LTV
- 2-way model unless strong reason for 1-way
- Tracking tool integrated and tested
- Anti-fraud measures live (5+)
- Reward delivery automated within 24h
- Legal compliance per region (TCPA / GDPR / PDPA / LGPD)
- Landing page for referees built
- K-factor target documented; measure at 30 / 60 / 90 days
Related skills
product-marketing-context-global— foundation14-email-marketing-global— email-driven referral mechanics27-personal-brand-monetize-global— affiliate / creator program (when available)references/global-legal-compliance— deep legal reference
Global Skill 18 (Referral Program) | Over Powers Agency | v1.0.0
© minhnv0807, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files in skills/en/18-referral-program-global of minhnv0807/ai-business-skills.
- SKILL.md
- variants/01-us.md
- variants/02-eu.md
- variants/03-sea.md
- variants/04-latam.md
Open the folder on GitHubat commit 0360adc
Compare with similar skills
18 Referral Program Global next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| 18 Referral Program Global this skillminhnv0807/ai-business-skills | 610 | — | ~3.8k | Automated safety check: Pass | MIT | |
| Churn Preventionfreekmurze/dotfiles | 1k | 13 repos | ~4.4k | Automated safety check: Pass | None | |
| Smscoreyhaines31/marketingskills | 54k | 1 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Email Sequence Designeraaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Reactivation Specialistaaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~4.1k | Automated safety check: Pass | Apache-2.0 | |
| Growth Enginesickn33/agentic-awesome-skills | 47k | 2 repos | ~2.2k | Automated safety check: Pass | MIT |
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Categories
Questions about 18 Referral Program Global
What does 18 Referral Program Global do?
A skill your agent uses when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and…. 18 Referral Program Global is an agent skill from minhnv0807/ai-business-skills. Use when customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and attribution, fraud controls, and anti-spam compliance by region: TCPA for US, GDPR for EU, PDPA for SEA, LGPD for LATAM.
When should I use 18 Referral Program Global?
18 Referral Program Global fits situations like: customers should bring other customers — one-way versus two-way incentives; reward structure and economics; trigger moments; referral messaging.
How do I install 18 Referral Program Global in Claude Code?
Run `npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a claude-code`. Or copy the skill folder (skills/en/18-referral-program-global in minhnv0807/ai-business-skills) into .claude/skills/18-referral-program-global in your project. Claude Code loads it when a task matches its description.
How do I install 18 Referral Program Global in Codex?
Run `npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a codex`. Or copy the skill folder (skills/en/18-referral-program-global in minhnv0807/ai-business-skills) into .agents/skills/18-referral-program-global in your project. Codex loads it when a task matches its description.
Can I use 18 Referral Program Global in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add minhnv0807/ai-business-skills --skill 18-referral-program-global -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/18-referral-program-global, .gemini/skills/18-referral-program-global, .github/skills/18-referral-program-global and .opencode/skills/18-referral-program-global in your project.
What does 18 Referral Program Global need to run?
SKILL.md names no scripts, command-line tools or credentials: 18 Referral Program Global is instructions for the agent only.
Does 18 Referral Program Global access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is 18 Referral Program Global safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does 18 Referral Program Global use?
18 Referral Program Global is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does 18 Referral Program Global use?
About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to 18 Referral Program Global?
Skills that share tags, products or a category with 18 Referral Program Global: Churn Prevention (freekmurze/dotfiles, 1k stars), Sms (coreyhaines31/marketingskills, 54k stars), Email Sequence Designer (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and Reactivation Specialist (aaron-he-zhu/aaron-marketing-skills, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains 18 Referral Program Global?
minhnv0807 (a GitHub user) maintains it in minhnv0807/ai-business-skills, which has 610 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on September 12, 2026.
Source: minhnv0807/ai-business-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.