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 RBraga01/builder-growth --skill experiment-designgit clone --depth 1 https://github.com/RBraga01/builder-growthWrote 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/rbraga01/builder-growth/experiment-design)<a href="https://agentmods.dev/skills/rbraga01/builder-growth/experiment-design"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-growth/experiment-design/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/rbraga01/builder-growth/experiment-design"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-growth/experiment-design.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 | $0.00058 | $0.01831 |
| Opus 5 | $0.00029 | $0.00915 |
| Sonnet 5 | $0.00012 | $0.00366 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
experiment-design 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 9d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design
The Law
AN EXPERIMENT WITHOUT A STOPPING RULE IS NOT AN EXPERIMENT — IT IS A FEATURE WAITING FOR PERMISSION.
"We'll run it for a while and see" produces results that stop when the team wants them to stop — which is when they look good.
Hypothesis + metric + sample size + duration + stopping rule + decision rule IS an experiment.
When to Use
Trigger before:
- Any A/B test on a growth surface (landing page, pricing, onboarding, email, activation flow)
- Any feature gate experiment where one group sees a different experience
- Any pricing or packaging test
- Any content variant test that informs a product or copy decision
When NOT to Use
- Qualitative research sessions (no control group, no statistical inference — use
user-research-synthesis) - Shadow mode deployments monitoring for regressions (not an experiment — monitoring)
- Product A/B tests covered by
ab-test-designin builder-product (use that skill for product features; use this skill for growth surfaces)
The Six Required Elements
Growth experiments share the same statistical requirements as product experiments. The difference is in the metrics and what "conversion" means.
1 — Hypothesis
One sentence with four parts: change, metric, direction + magnitude, mechanism.
If we [specific change to the control experience],
then [primary conversion metric] will [increase/decrease] by at least [MDE]%,
because [causal mechanism — why this change affects this metric].
The mechanism matters for learning. If the test result matches the hypothesis but the mechanism was wrong, you cannot predict whether the same change will work elsewhere.
2 — Primary Metric
The single metric that determines ship or no-ship.
Growth-specific metric types:
- Acquisition: signup rate, trial start rate, demo request rate
- Activation: completion of first key action, feature adoption rate
- Retention: D7, D30, D90 retention, churn rate
- Revenue: conversion to paid, ARPU, LTV (longer test duration required)
- Referral: invite rate, viral coefficient
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.
- 9d ago First seen · 179 lines · 58 tokens per session scan A 98f9723c24c4
experiment-design is a skill published in the GitHub repository RBraga01/builder-growth (2 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 1,831 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 2026-08-31.
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