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.
git clone --depth 1 https://github.com/rajitsaha/100xprismWrote 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/rules/rajitsaha/100xprism/referral-program)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/referral-program"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/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/rules/rajitsaha/100xprism/referral-program"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/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 | $0.00033 | $0.01442 |
| Opus 5 | $0.00016 | $0.00721 |
| Sonnet 5 | $0.00007 | $0.00288 |
| Haiku 4.5 | $0.00003 | $0.00144 |
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 5d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referral & Affiliate Programs
Design and optimize programs that turn customers into growth engines.
Before Starting
Product context: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it first and tailor output to it; only ask for what it doesn't cover.
Gather (ask if not provided):
- Program Type — customer referral, affiliate, or both? B2B or B2C? Average customer LTV? Current CAC from other channels?
- Current State — existing program? Referral rate (% who refer)? Incentives tried?
- Product Fit — shareable product? Network effects? Do customers naturally talk about it?
- Resources — tools/platforms in use or considered? Budget for incentives?
Referral vs. Affiliate
Customer referral programs — best for existing customers recommending to their network, products with natural word-of-mouth, lower-ticket or self-serve products. Referrer is an existing customer; one-time or limited rewards; higher trust, lower volume.
Affiliate programs — best for reaching audiences you don't have access to (content creators, influencers, bloggers) and higher-ticket products that justify commissions. Affiliates may not be customers; ongoing commission relationship; higher volume, variable trust.
Referral Program Design
The Referral Loop
Trigger Moment → Share Action → Convert Referred → Reward → (Loop)
Step 1: Identify Trigger Moments
High-intent moments: right after first "aha" moment, after achieving a milestone, after exceptional support, after renewing or upgrading.
Step 2: Design Share Mechanism
Ranked by effectiveness:
- In-product sharing (highest conversion)
- Personalized link
- Email invitation
- Social sharing
- Referral code (works offline)
Step 3: Choose Incentive Structure
- Single-sided (referrer only): simpler, works for high-value products
- Double-sided (both parties): higher conversion, win-win framing
- Tiered: gamifies referrals, increases engagement
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.
- 5d ago First seen · 192 lines · 1,442 tokens per session scan A bac73e9d3984
referral-program is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 8d ago), licensed MIT. It adds 33 tokens to every session and 1,442 once invoked, about $0.0002 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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