Growth experiments
A founder/indie-hacker skills pack for Claude Code, Cursor, Codex and Gemini CLI: market research, competitor analysis, outcome-based pricing, landing-page copy, MVP spec, cold outreach, growth experiments and fundraising narrative.
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One thing to look at
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What its author says it does
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Designs and prioritizes growth experiments with ICE scoring and a weekly experiment loop. Trigger on "growth experiments", "how do I grow", "prioritize growth ideas", "ICE score", "set up a growth loop", or "what should I test to get more users".
SKILL.md
2.2 KB, as published. Nobody here has run it
When to use
When a founder has traffic or early users and needs a disciplined way to find what moves the needle — instead of random tactics.
Inputs to gather
- The one metric that matters most right now (activation, signup→paid, retention, referral).
- Current funnel numbers (even rough) and the biggest drop-off step.
- The primary acquisition channel and weekly bandwidth for experiments.
Process
- Find the bottleneck stage in the funnel (acquisition → activation → revenue → retention → referral). Run experiments where the leak is biggest, not where it's easy.
- Generate 8-12 experiment ideas tied to that stage, each as a hypothesis: "If we [change], then [metric] improves because [reason]."
- ICE-score each: Impact, Confidence, Ease (1-10 each); score = average. Sort desc.
- Pick the top 1-3 that fit this week's bandwidth. Define the success threshold and minimum sample size before running — so the result is unambiguous.
- Run the weekly loop: Mon pick & define → run → Fri read result → keep / kill / double-down → log the learning. Compound the wins.
- Channel focus: go deep on one channel until it plateaus before opening a second — distribution beats spreading thin.
Output
- Funnel snapshot with the bottleneck stage flagged.
- Experiment backlog table: hypothesis · metric · ICE (I/C/E + score) · effort.
- This week's 1-3 experiments with success threshold and sample size each.
- Weekly loop template to reuse, with a running learnings log.
Quality bar & pitfalls
- Good = every experiment is a falsifiable hypothesis with a pre-set success bar.
- Score honestly — Confidence should reflect evidence, not enthusiasm.
- Avoid: vanity metrics (raw signups over activated users); running 10 things at once so nothing is conclusive; chasing new channels before exhausting the working one; no learnings log (you'll re-run dead experiments).