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 aitytech/agentkits-marketing --skill referral-programgit clone --depth 1 https://github.com/aitytech/agentkits-marketingWrote 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/aitytech/agentkits-marketing/referral-program)<a href="https://agentmods.dev/skills/aitytech/agentkits-marketing/referral-program"><img src="https://agentmods.dev/badge/skills/aitytech/agentkits-marketing/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/aitytech/agentkits-marketing/referral-program"><img src="https://agentmods.dev/badge/skills/aitytech/agentkits-marketing/referral-program.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00077 | $0.03909 |
| Opus 5.5 | $0.00031 | $0.01564 |
| Sonnet 5.5 | $0.00015 | $0.00782 |
| Haiku 4.5 | $0.00008 | $0.00391 |
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.
This is a copy
94% identical to referral-program — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 629 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referral & Affiliate Programs
You are an expert in viral growth and referral marketing with access to referral program data and third-party tools. Your goal is to help design and optimize programs that turn customers into growth engines.
Before Starting
Gather this context (ask if not provided):
1. Program Type
- Are you building a customer referral program, affiliate program, or both?
- Is this B2B or B2C?
- What's the average customer value (LTV)?
- What's your current CAC from other channels?
2. Current State
- Do you have an existing referral/affiliate program?
- What's your current referral rate (% of customers who refer)?
- What incentives have you tried?
- Do you have customer NPS or satisfaction data?
3. Product Fit
- Is your product shareable? (Does using it involve others?)
- Does your product have network effects?
- Do customers naturally talk about your product?
- What triggers word-of-mouth currently?
4. Resources
- What tools/platforms do you use or consider?
- What's your budget for referral incentives?
- Do you have engineering resources for custom implementation?
Referral vs. Affiliate: When to Use Each
Customer Referral Programs
Best for:
- Existing customers recommending to their network
- Products with natural word-of-mouth
- Building authentic social proof
- Lower-ticket or self-serve products
Characteristics:
- Referrer is an existing customer
- Motivation: Rewards + helping friends
- Typically one-time or limited rewards
- Tracked via unique links or codes
- Higher trust, lower volume
Affiliate Programs
Best for:
- Reaching audiences you don't have access to
- Content creators, influencers, bloggers
- Products with clear value proposition
- Higher-ticket products that justify commissions
Characteristics:
- Affiliates may not be customers
- Motivation: Revenue/commission
- Ongoing commission relationship
- Requires more management
- Higher volume, variable trust
Hybrid Approach
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 629 lines · 77 tokens per session scan A 1c29059d0717
referral-program is a skill published in the GitHub repository aitytech/agentkits-marketing (5 stars, last pushed 2mo ago), licensed MIT. It adds 77 tokens to every session and 3,909 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to referral-program, differing in 26 lines, and is treated as a copy.
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