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 agentmods add skills/moizibnyousaf/marketing-cli/referral-programnpx skills add MoizIbnYousaf/marketing-cli --skill referral-programgit clone --depth 1 https://github.com/MoizIbnYousaf/marketing-cliWrote 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/moizibnyousaf/marketing-cli/referral-program)<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/referral-program"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/referral-program.svg" alt="Measured on agentmods" 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 | $0.00160 | $0.02764 |
| Opus 5 | $0.00080 | $0.01382 |
| Sonnet 5 | $0.00032 | $0.00553 |
| Haiku 4.5 | $0.00016 | $0.00276 |
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 yesterday.
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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referral Program Design
Purpose
Design a referral program that turns existing users into an acquisition channel. Define the incentive model, sharing mechanics, copy, and launch plan. Focus on programs that actually get used — not "refer a friend" links that collect dust.
Reads
brand/audience.md— Personas, what they value, their networksbrand/positioning.md— Value props, pricing context for incentive sizingbrand/voice-profile.md— Brand voice for all referral copy
Brand Integration
- audience.md — Incentive type depends on audience. B2B audiences prefer account credits. B2C audiences prefer discounts or free months. Developer audiences prefer swag or extended trials.
- positioning.md — Referral messaging reinforces the brand's positioning angle, not generic 'share with a friend' copy. If positioning is around simplicity, the referral CTA is 'Know someone drowning in complexity?'
- voice-profile.md — All referral copy (dashboard prompts, share text, emails) should match the brand voice. A casual brand writes "Your friend's gonna love this" while a professional brand writes "Share a professional recommendation."
Workflow
Step 1: Choose Referral Model
Evaluate and recommend the right model:
One-Sided (Referrer only gets reward)
- Best for: High-value products, enterprise, low-frequency purchases
- Pros: Simple, lower cost per referral
- Cons: Less motivation for referee to convert
- Example: "Give your friend our link, you get $50 credit"
Two-Sided (Both get reward)
- Best for: SaaS, marketplaces, subscription products
- Pros: Higher conversion (referee has incentive), feels fair
- Cons: Higher cost per referral
- Example: "Give $20, get $20"
Tiered (Rewards increase with referral count)
- Best for: Products with power users, community-driven products
- Pros: Creates referral champions, gamification
- Cons: Complex to communicate, top-heavy rewards
- Example: "1 referral = 1 month free. 5 = premium forever."
What ships with it
3 files 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.
- yesterday First seen · 292 lines · 160 tokens per session scan A b4a24102795b
referral-program is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 18d ago), licensed MIT. It adds 160 tokens to every session and 2,764 once invoked, about $0.0008 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 2026-09-03.
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