Referral Program

Community

kostja94

referral-program

When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral incentives," or "viral loop." For referral landing copy, use landing-page-generator.

Overview

Publisher	kostja94
Repository	`marketing-skills`
Skill name	`referral-program`
Stars	979
Forks	137
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by kostja94 on GitHub. Read the source before you install it.

Installation

Install the Referral Program AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/kostja94/marketing-skills/tree/main/skills/channels/partnerships/referral-program

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/kostja94/marketing-skills.git /tmp/marketing-skills
mkdir -p .claude/skills
cp -r /tmp/marketing-skills/skills/channels/partnerships/referral-program .claude/skills/referral-program

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Referral Program in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Referral Program on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Referral Program is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Channels: Referral

Guides referral program strategy for AI/SaaS products. Leverage existing users to drive growth; 3%-5% conversion vs 1%-2% for ads; CAC 50%-70% lower; referred users LTV 30%-50% higher, retention 20%-30% higher. Referral is necessity in overseas markets, not alternative.

When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Initial Assessment

Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product, audience, and value proposition.

Identify:

Product type: SaaS, AI tool, subscription
User base: Size, engagement, retention
Goal: Signups, purchases, or both

Referral vs. Affiliate vs. Influencer

Dimension	Referral	Affiliate	Influencer
Who	Existing users	Professional promoters	KOLs
Incentive	Discounts, credits	Commission	Fees, product
Barrier	Low (all users)	Medium	High
Conversion	3%-5%	Varies	Varies

Referral vs affiliate: Referral needs no landing page or application; integrated in dashboard. Affiliate requires landing page and approval.

Reward Models

Model	Use
Two-way	Both referrer and referee get rewards; highest participation
One-way	Only referrer rewarded; cost control
Tiered	Rewards increase with referral count (e.g. $10 for 1-5, $15 for 6-10, $20 for 11+); incentivizes volume

Benchmark: Rewards typically 10%-30% of product price; ~11% off or ~$21 value; weak incentives = low participation. Triggers: signup, purchase, activation, or sustained use.

Mechanism Types

Type	Use
Link-based	Unique referral link; easy to implement; accurate tracking; share via email, social, SMS; works for web and app
Code-based	Referral code (e.g. FRIEND20); memorable; offline events; mobile-friendly input
Social referral	Share buttons (Facebook, X, LinkedIn); viral spread; friend trust; young users

Tracking & Attribution

Method	Use
Cookie	Web apps; 30-90 day window
URL params	All platforms; persistent in link
Referral code	Mobile, offline; manual entry
Account association	Long-term tracking; subscription products

Attribution window: 30-90 days typical; 180 days for subscription. First-touch attribution to avoid double-counting.

Fraud Prevention

Risk	Action
Self-referral	Detect same device, payment, IP
Fake accounts	Validate email, payment; monitor patterns
Bulk/automation	Rate limits; anomaly detection
Per-user cap	e.g. Max 10 referrals per user

Use tool anti-fraud features; audit referrals regularly.

Design Framework

Reward structure: Type (cash, discount, credits, free service); amount (10%-30% of price); trigger; cap
Tracking: Choose method; set attribution window; first-touch rule
UX: One-click share; clear rules; dashboard with referral data; notify on success
Fraud prevention: See above
Monitor & optimize: Referral rate, conversion, CAC, LTV; A/B test rewards and flow

Best Practices

Run multiple programs: Target different audiences, stages, goals
Tiered rewards: Motivate top performers; progressive incentives
Friction-free sharing: Mobile-friendly; one-click share
Time-boxed incentives: "Refer this week for $15 off" creates urgency
Placement: Web, email, app, in-product touchpoints; dashboard integration primary

Implementation

Approach	Use
Self-build	Full control; low cost; URL params or cookie + reward logic + fraud checks; open-source (e.g. RefRef) for faster start
Third-party	Fast launch; Cello, Viral Loops, ReferralCandy (e-commerce), Impact (enterprise); monthly fee

Placement: Most programs integrate in product dashboard; no landing page or application needed. Optional landing page for value prop, rewards, and case studies.

Startup cost: Typically hundreds for tools + dev.

Tools

Tool	Use
Cello	SaaS; AI-driven automation
Viral Loops	Referral + waitlist + contests
ReferralCandy	Shopify, e-commerce
Impact	Enterprise; unified platform
RefRef	Open-source; self-hosted

KPIs

Referral rate, conversion, CAC, LTV of referred users, referred-user retention.

Output Format

Reward model and mechanism type (link/code/social)
Tracking approach and attribution window
Placement (dashboard vs landing page)
Fraud prevention measures
Tool selection (self-build vs third-party)
KPI framework

Related Skills

discount-marketing-strategy: Referral rewards (discounts, credits); 10–30% benchmark; campaign design
affiliate-marketing: Different audience; can run both
influencer-marketing: Brand building vs. user-driven growth
directory-submission: Directory submission for discovery; referral for user-driven growth
analytics-tracking: Referral link tracking, UTM

Frequently asked questions

What does the Referral Program AI skill do?

Why use Referral Program on TypingMind?

Because you install it once and use it with any model. Referral Program is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Referral Program in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kostja94/marketing-skills/tree/main/skills/channels/partnerships/referral-program. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Referral Program?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Referral Program?

As many as you like. As long as a model supports skills, you can use Referral Program with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Referral Program AI skill free?

Yes. It is published on GitHub by kostja94 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Channels: Referral

Initial Assessment

Referral vs. Affiliate vs. Influencer

Reward Models

Mechanism Types

Tracking & Attribution

Fraud Prevention

Design Framework

Best Practices

Implementation

Tools

KPIs

Output Format

Related Skills

Frequently asked questions

Related AI Skills

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