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Growth experiment planner

Skill sisodiabhumca/agent-skills/skills/growth-experiment-planner

Use when planning A/B tests in LaunchDarkly, Optimizely, or similar platforms. Sizes the experiment (sample size, MDE, runtime), drafts hypothesis + success metrics + guardrails, and produces a launch checklist + rollback plan.From its SKILL.md

Install
npx -y skills add sisodiabhumca/agent-skills --skill growth-experiment-planner

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

1.8 KB, 365 tokens by cl100k_base, as published. Nobody here has run it

Growth Experiment Planner

When to invoke

  • "Plan an A/B test for the new onboarding flow."
  • "How long do we need to run this experiment?"
  • "Draft an experiment brief for the pricing page test."

Inputs needed

  1. Hypothesis — what change, expected effect, why.
  2. Primary metric — name, baseline rate or mean, variance if known.
  3. Traffic — daily users hitting the surface.
  4. MDE — minimum detectable effect (relative).
  5. Variants — control + N treatments.
  6. Guardrails — metrics that must NOT regress (revenue, errors, latency).

Workflow

  1. Frame — restate hypothesis in one sentence.
  2. Size — call plan.py to compute sample size and runtime.
  3. Spec — generate experiment brief: metrics, segments, allocation, stopping rules, guardrails.
  4. Checklist — pre-launch QA, holdout, instrumentation, rollback path.
  5. Hand off — output a Markdown brief ready for LaunchDarkly/Optimizely.

Output format

A complete experiment brief with: Hypothesis, Variants, Metrics, Sample size, Runtime, Allocation, Guardrails, Stopping rules, QA checklist, Rollback plan.

Guardrails

  • Always require a primary metric defined before launch (no metric fishing).
  • Require explicit guardrails — at minimum: error rate, p95 latency, revenue per user.
  • Flag if runtime exceeds 4 weeks (novelty + seasonality risk).

Reference code

plan.py computes two-proportion sample size (Evan Miller formula) or two-sample t-test sample size.

What ships with it: 2 files

6.1 KB alongside SKILL.md, 1 of them executable

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