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 LeadMagic/gtm-skills --skill growth-experimentationgit clone --depth 1 https://github.com/LeadMagic/gtm-skillsWrote 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/leadmagic/gtm-skills/growth-experimentation)<a href="https://agentmods.dev/skills/leadmagic/gtm-skills/growth-experimentation"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/growth-experimentation/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/leadmagic/gtm-skills/growth-experimentation"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/growth-experimentation.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 | $0.00092 | $0.01474 |
| Opus 5 | $0.00046 | $0.00737 |
| Sonnet 5 | $0.00018 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00147 |
Grade A, and why
growth-experimentation 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Experimentation
Overview
The companies with the highest growth rates don't have better ideas — they have better systems for testing ideas. A high-velocity experimentation system runs 15-30 experiments per month across acquisition, activation, retention, and monetization. Most experiments fail. That's by design. The team that learns fastest from each failure wins.
When to Use
- "Build an experimentation program"
- "Set up growth sprints"
- "Prioritize experiments with ICE"
- "Increase our test velocity"
- "Create a learning repository"
Authoritative Foundations
- Sean Ellis & Morgan Brown (Hacking Growth) — coined "growth hacking." North Star Metric. Growth experimentation loop: analyze → ideate → prioritize → test → learn.
- Brian Balfour (Reforge, ex-HubSpot VP Growth) — increasing HubSpot's experiment velocity from 5 to 20/week produced 3x growth rate improvement. Four Fits Framework: Market-Product, Product-Channel, Channel-Model, Model-Market.
- Andrew Chen (a16z, ex-Uber Growth) — The Cold Start Problem. Growth teams at scale.
- Fareed Mosavat (Reforge, ex-Slack Growth) — experimentation systems.
Step-by-Step Process
Phase 1: Set the North Star Metric
One metric that captures core value delivery. If this moves up, the business is healthier. All experiments ladder to this metric.
Phase 2: ICE Scoring
Score every experiment idea 1-10 on Impact, Confidence, Ease. Average the three. Prioritize by ICE score. Re-score weekly as new data arrives.
Phase 3: Growth Sprint Cadence
Weekly cycle: idea generation (Monday), prioritization (Tuesday), build (Wed-Thu), launch (Fri), analyze (Mon). 2-week sprints for complex tests. AI compresses cycle: a single growth marketer with AI can test 10 variants in time it used to take to build one.
Phase 4: Experiment Design
Every experiment: hypothesis, success metric, minimum detectable effect, required sample size, maximum duration. Document everything — winners and losers. Build a searchable learning repository.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed 6c667cfdb0ed
- 9d ago First seen · 148 lines · 92 tokens per session scan A e8b4276e207f
growth-experimentation is a skill published in the GitHub repository LeadMagic/gtm-skills (48 stars, last pushed yesterday), licensed MIT. It adds 92 tokens to every session and 1,474 once invoked, about $0.0005 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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