referral-program

referral-program is a skill for Claude Code, Codex from varunk130/ai-gtm-skill-library. It costs 62 tokens per session (1,414 once invoked), scanned A, original, MIT.

A referral-program planning framework designs how existing users invite new users and how both sides are rewarded.

In plain words
What is it for?
It helps define rewards, invitation steps, where referral requests appear, trust signals, viral growth measures, fraud controls, and referral attribution.
Why use it?
It addresses the practical problems of low participation, unclear incentives, fraud, and difficulty measuring whether referrals add new business.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps define rewards, invitation steps, where referral requests appear, trust signals, viral growth measures, fraud controls, and referral attribution.

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Install with agentmods
npx agentmods add skills/varunk130/ai-gtm-skill-library/referral-program
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add varunk130/ai-gtm-skill-library --skill referral-program
Clone the repo
git clone --depth 1 https://github.com/varunk130/ai-gtm-skill-library

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for referral-program

README.md
[![agentmods](https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/referral-program/github.svg)](https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/referral-program)
Your own site
<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/referral-program"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/referral-program/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for referral-program

Your own site · 80×15
<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/referral-program"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/referral-program.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,414 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.01414
Opus 5 $0.00031 $0.00707
Sonnet 5 $0.00012 $0.00283
Haiku 4.5 $0.00006 $0.00141

Measured 8d ago against content hash d11850208da6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

referral-program scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

revops-skills/referral-program/SKILL.md · 126 lines

How it starts

The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Referral Program (RIPPLE Framework)

Design a referral program with a real viral mechanic - not a "refer a friend" button buried in settings. RIPPLE forces explicit design of who refers, why they refer, what the receiver gets, where the program lives, and how it's measured against a viral coefficient.

Core Principle

Referral programs fail because they optimize for the sender's reward and ignore the receiver's trust. A high-K loop requires both. RIPPLE designs both sides of the exchange and instruments the loop end-to-end.

The RIPPLE Framework

Letter Stage The Question
R Reward Architecture What does the referrer get, what does the referee get, and when?
I Invite Mechanic How is the invite sent, and how low-friction is the share?
P Placement Where in the product / journey does the ask appear?
P Proof What social proof and trust signals accompany the invite?
L Loop Math What's the viral coefficient target, and which lever moves it?
E Evaluate & Defend How is fraud, cannibalization, and incremental lift measured?

Reward Architecture

The most common failure mode is single-sided rewards.

Type Pattern Best For
Double-sided Both referrer and referee get reward Most consumer / SMB programs
Single-sided (referrer) Only referrer rewarded Pure-advocacy programs (low conversion lift)
Single-sided (referee) Only referee rewarded When referrer reward feels mercenary (e.g., enterprise)
Tiered Reward escalates with N successful referrals Power-user motivation

Reward type considerations:

Reward Pros Cons
Cash / credit Simple, easy attribution Attracts abuse, low brand lift
Product credit Reinforces product use Less appealing if not active user
Account upgrade Aligns with retention Limited liability cap
Cause donation High-trust, brand-aligned Smaller activation lift
Exclusive access Status-driven, low cost Niche appeal

Read the full file on GitHub · 126 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 126 lines · 62 tokens per session scan A d11850208da6

Subscribe to this mod's changes

referral-program is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (5 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 1,414 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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