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 RevenueCat/ai-toolkit --skill revenuecat-experimentsgit clone --depth 1 https://github.com/RevenueCat/ai-toolkitWrote 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/revenuecat/ai-toolkit/revenuecat-experiments)<a href="https://agentmods.dev/skills/revenuecat/ai-toolkit/revenuecat-experiments"><img src="https://agentmods.dev/badge/skills/revenuecat/ai-toolkit/revenuecat-experiments/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/revenuecat/ai-toolkit/revenuecat-experiments"><img src="https://agentmods.dev/badge/skills/revenuecat/ai-toolkit/revenuecat-experiments.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 | $0.00057 | $0.00987 |
| Opus 5 | $0.00028 | $0.00494 |
| Sonnet 5 | $0.00011 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00099 |
Grade A, and why
revenuecat-experiments 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 13d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setting up and managing experiments
A RevenueCat experiment compares two to four offerings: offering_a (the control) against offering_b–offering_d (the treatments). Enrolled customers are served their variant's offering instead of the project's current offering. Setting up an experiment means preparing one offering per variant, then creating the experiment as a draft.
Refer to the MCP tool schemas for the exact parameters of each tool; the create-experiment schema also lists the available experiment types with their recommended primary and secondary metrics.
Preparing the variants
Keep the variants identical except for the one aspect under test, otherwise results cannot be attributed to the change. For example, when measuring the impact of a lower subscription price, both variants should share the same paywall design and packages, differing only in the price of the products.
Control. When the control variant is "what production does today", pass the current offering (is_current in list-offerings) directly as offering_a_id — do not duplicate it. Duplicate only when control itself needs changes relative to production.
Treatments. First check with list-offerings whether an offering already exists that matches what the treatment should serve, and reuse it instead of duplicating. Otherwise, start each treatment from a duplicate of the offering closest to it (usually the control offering). duplicate-offering copies the packages (attaching the same existing products) and, if the source offering has a paywall, also copies it as a new unpublished draft; it returns the new offering, including its new paywall_id. Then apply the one change under test:
- Paywall change (copy, layout, CTA, pricing display, ...): this requires the app to use RevenueCat Paywalls — a paywall is an optional property of an offering, and if the control offering has none (
paywall_idisnull). Otherwise, edit the duplicated paywall withedit-paywall-ai. Request only the minimum changes required to measure the desired effect, and verify the result by looking at the resulting paywall. - Price, trial, or introductory-offer change: these live on store products, so the treatment offering needs different products. Check with
list-productswhether suitable products already exist; create only the missing ones and their store counterparts (see therevenuecat-store-stateskill). Then swap them into the duplicated offering's packages: detach the copied products withdetach-products-from-package, then attach the new ones withattach-products-to-package. - Brand-new paywall: duplicate the offering with
include_paywall: false, then callcreate-paywall-aiwith the new offering'soffering_idso the generated draft is attached to it directly. A paywall and an offering pair 1:1 —attach-offering-to-paywallfails if either side is already paired; undo a wrong pairing withdetach-offering-from-paywall(unpublish the paywall first if it is published).
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.
- 13d ago First seen · 40 lines · 57 tokens per session scan A 2293c5f9c129
revenuecat-experiments is a skill published in the GitHub repository RevenueCat/ai-toolkit (65 stars, last pushed 10d ago), licensed MIT. It adds 57 tokens to every session and 987 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 2026-08-30.
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