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/growth-experimentgit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-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/commands/brainbytes-dev/everything-claude-marketing/growth-experiment)<a href="https://agentmods.dev/commands/brainbytes-dev/everything-claude-marketing/growth-experiment"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-marketing/growth-experiment.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 | $0.00022 | $0.01952 |
| Opus 5 | $0.00011 | $0.00976 |
| Sonnet 5 | $0.00004 | $0.00390 |
| Haiku 4.5 | $0.00002 | $0.00195 |
Grade A, and why
growth-experiment 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 3d 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/growth-experiment
Design a structured growth experiment with a clear hypothesis, ICE prioritization score, defined metrics, and success criteria — ready to run and measure.
What This Command Does
This command takes a growth idea and transforms it into a rigorous experiment framework. Instead of just launching features and hoping for the best, it structures your test with a falsifiable hypothesis, defines the minimum sample size for statistical significance, sets primary and guardrail metrics, establishes clear success criteria before the experiment begins, and creates a timeline with decision points. The output follows the scientific method applied to growth — so you learn something valuable whether the experiment succeeds or fails.
When to Use
- Testing a new feature's impact on activation, retention, or revenue
- Evaluating a change to onboarding flow, pricing, or packaging
- Running A/B tests on landing pages, email sequences, or ad creative
- Validating a referral, loyalty, or viral growth mechanic
- Testing a new acquisition channel before committing significant budget
- Experimenting with pricing models, trial lengths, or freemium limits
- Prioritizing a backlog of growth ideas using a consistent framework
- Building a culture of experimentation within your team
How It Works
- Captures the idea — Understands what you want to test and the business context behind it
- Formulates the hypothesis — Structures a falsifiable hypothesis with expected outcome and mechanism
- Scores with ICE — Rates the experiment on Impact, Confidence, and Ease to help you prioritize
- Defines metrics — Sets one primary metric, supporting metrics, and guardrail metrics that must not degrade
- Calculates sample size — Determines how many users or events you need for statistically significant results
- Sets success criteria — Defines what "winning" looks like before the experiment runs, eliminating post-hoc rationalization
- Plans execution — Outlines implementation steps, required resources, and timeline
- Creates the decision framework — Defines what happens if the experiment wins, loses, or is inconclusive
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.
- 3d ago First seen · 220 lines · 22 tokens per session scan A 1f5ac69cb274
growth-experiment is a command published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 1,952 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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