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.
npx skills add varunk130/ai-gtm-skill-library --skill referral-programgit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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.
[](https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/referral-program)<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.
<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>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.01414 |
| Opus 5.5 | $0.00025 | $0.00566 |
| Sonnet 5.5 | $0.00012 | $0.00283 |
| Haiku 4.5 | $0.00006 | $0.00141 |
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 1mo 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.
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 |
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.
- 1mo ago First seen · 126 lines · 62 tokens per session scan A d11850208da6
referral-program is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 62 tokens to every session and 1,414 once invoked, about $0.0002 per session on Opus 5.5. 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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