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 commands/brainbytes-dev/everything-claude-marketing/ab-testgit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWhat 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 | $0.00019 | $0.01667 |
| Opus 5 | $0.00010 | $0.00834 |
| Sonnet 5 | $0.00004 | $0.00333 |
| Haiku 4.5 | $0.00002 | $0.00167 |
Grade A, and why
ab-test 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 2d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ab-test
Design rigorous A/B tests with structured hypotheses, defined success metrics, calculated sample size requirements, estimated test durations, and analysis plans.
What This Command Does
The /ab-test command takes your testing idea and transforms it into a methodologically sound experiment design. It ensures your test has a clear hypothesis, measurable outcomes, sufficient statistical power, and a defined analysis plan before you invest development and traffic resources. The output is a complete test brief that your engineering, design, and analytics teams can execute against.
The command delegates to the cro-specialist agent, which applies conversion rate optimization expertise and statistical testing methodology to design experiments that produce reliable, actionable results.
When to Use
- You want to test a new page design, layout, or content variation against the current version
- You are optimizing conversion rates on landing pages, signup flows, or checkout processes
- You need to validate a hypothesis about user behavior before committing to a full redesign
- You want to test pricing page layouts, CTA copy, or value proposition framing
- You are running email subject line tests and need proper methodology
- You want to test changes to onboarding flows or feature adoption prompts
- You need to justify a proposed change to stakeholders with data
How It Works
- Hypothesis Formulation — Structures your test idea into a formal hypothesis with independent variable, dependent variable, and expected outcome
- Metric Definition — Defines primary and secondary success metrics with clear measurement methods
- Variant Design — Specifies exactly what changes between control and treatment, and why those changes are expected to impact the metric
- Sample Size Calculation — Determines how many visitors or users you need for statistically significant results at your desired confidence level
- Duration Estimation — Estimates how long the test needs to run based on your traffic volume and required sample size
- Segmentation Plan — Identifies audience segments to analyze separately for heterogeneous treatment effects
- Analysis Plan — Defines how results will be evaluated, including what constitutes a winner and when to stop the test
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.
- 2d ago First seen · 194 lines · 19 tokens per session scan A 87e10ffdbdeb
ab-test is a command published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 1,667 once invoked, about $0.0001 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.
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