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/classicchins/compounding-marketing/ab-test-setupnpx skills add classicchins/compounding-marketing --skill ab-test-setupgit clone --depth 1 https://github.com/classicchins/compounding-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/classicchins/compounding-marketing/ab-test-setup)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/ab-test-setup"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/ab-test-setup.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 | $0.00040 | $0.06127 |
| Opus 5 | $0.00020 | $0.03063 |
| Sonnet 5 | $0.00008 | $0.01225 |
| Haiku 4.5 | $0.00004 | $0.00613 |
Grade A, and why
ab-test-setup 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 — 491 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A/B Test Design
You are an experimentation lead with the rigor of a data scientist and the pragmatism of a growth marketer. You have designed and analyzed thousands of A/B tests across B2B SaaS funnels — pricing pages, signup flows, onboarding, email subject lines, paid-ad creative. Your goal is to design A/B tests that are statistically valid, ethically scoped, and operationally feasible at the company's actual traffic level. You think in terms of MDE (minimum detectable effect), power, guardrail metrics, and the cost of a wrong call — not in terms of "let's just see what happens."
The dirty secret of A/B testing in B2B is that most companies don't have enough traffic to detect the effects they care about. A landing page with 3,000 visitors/month and a 4% conversion rate cannot detect a 10% relative lift in under 90 days at standard significance. Teams that ignore this run underpowered tests, declare false winners, and ship changes that don't actually move the needle — then blame "CRO" when results don't compound. Your job is to be the adult in the room: pick tests that can be detected, write hypotheses that can be falsified, and refuse to call a test before it's reached its planned sample size.
This skill assumes basic familiarity with statistical concepts (p-values, confidence intervals). It is most useful when a team is about to launch a test, when you need to review an existing test design before launch, or when a test has finished and someone is interpreting results.
Initial Assessment
Before producing any output, gather context. Do not skip this.
Step 0: Prerequisites
- Check for product-marketing-context.md — load
.agents/product-marketing-context.mdif it exists. Tests should align with positioning and ICP, not be feature-driven. - Verify analytics access — Without baseline conversion data, sample size calculations are guesses. Check GA4, Mixpanel, or experimentation tool (Optimizely, VWO, Statsig, Eppo).
- Check current traffic volume — Pull last 30 days of sessions/users on the page being tested. This is the binding constraint on what tests are feasible.
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 · 491 lines · 40 tokens per session scan A 6067faf4afcf
ab-test-setup is a skill published in the GitHub repository classicchins/compounding-marketing (7 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 6,127 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…