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Lifecycle Experiment Planner

Design lifecycle A/B tests with hypothesis checks, sample-size guidance, and stopping criteria.

DocumentationAgent instructionsRequirements & limits

Use when a lifecycle experiment needs statistical assumptions, stop rules, and guardrail metrics defined clearly.

Inputs

  • baseline_rate
  • minimum_detectable_effect
  • confidence_level (default 95)
  • power (default 80)

Outputs

  • Hypothesis plan
  • Sample-size guidance
  • Stop criteria

Before you begin

  • Reading and copying the procedure require no terminal setup. Check requirements for any packaged programs separately.

Try this first

Read the procedure and supply its documented inputs. Use the full text in an agent app or chat that supports pasted instructions, following that host's guidance.

Limits for this step
  • Instruction package only; it does not provide or authorize an executable runtime capability.
  • Safe for planning; require human approval before launching lifecycle experiments or changing user journeys.
  • Packaged programs have independent setup and verification requirements. Reading or copying this procedure does not verify their execution.
Read full instructionsHide full instructionsSKILL.md · v0.1.0

Lifecycle Experiment Planner

When to use

Use when planning new A/B tests for lifecycle journeys (email, push, landing page, messaging cadence).

Inputs required

  • baseline_rate
  • minimum_detectable_effect
  • confidence_level (default 95)
  • power (default 80)

Workflow

  1. Validate test objective and hypothesis.
  2. Define primary and guardrail metrics.
  3. Calculate minimum sample size.
  4. Produce plan with timeline and stopping rules.

Output format

  • Hypothesis statement
  • Experiment design table
  • Sample-size recommendation
  • Stop/continue criteria

Guardrails

  • Require one primary metric.
  • Reject vague hypotheses.
  • Avoid early stopping without guardrail breach.
View file metadata
---
name: lifecycle-experiment-planner
description: Design lifecycle A/B tests with hypothesis quality checks, sample-size guidance, stopping rules, and guardrail metrics.
---

Summary sources · Catalogue metadata, skill.yaml, SKILL.md · v0.1.0 · Release package

Install instruction files

These commands copy guidance into the selected runtime directories. Confirm that your agent app discovers it. Any packaged programs need separate setup and verification.

Install (Codex)

./bin/skills-hub install marketing/[email protected] --runtime codex

Install (Claude)

./bin/skills-hub install marketing/[email protected] --runtime claude

Install (Generic)

./bin/skills-hub install marketing/[email protected] --runtime generic --target ./my-agent/skills

How You Can Use This

Documentation

Resource form: Agent instructions

Read or copy the guidance. Any packaged programs have separate execution requirements and evidence.

First run: Read SKILL.md and follow the documented workflow.

Current limits

  • Instruction package only; it does not provide or authorize an executable runtime capability.

Packaged executable helpers

Each program has its own setup requirements and operational evidence. Reading or copying the instructions does not verify these programs.

scripts/sample_size.py · Not verified · Local program

First run: python3 scripts/sample_size.py 0.05 0.01

  • Packaged helper exists, but current clean-client executable evidence is not recorded.
What installation and verification mean
File installation
Places package files and generated configuration in the selected directories.
Native discovery
Your agent app finds and accepts the instructions, tools or plugin registrations.
Mock execution
Exercises sample fixtures. Connected data and provider effects need a separate check.
Connected operation
Requires current configuration, provider authentication, scoped permissions and a successful operation in your intended environment.

Declared compatibility

Compatibility unknown: versions and capabilities have not been declared.

Classification: declared

Technical details and release files

Operational Summary

Execution mode: may-run-local-verification

Approval boundary: Safe for planning; require human approval before launching lifecycle experiments or changing user journeys.

Review and lifecycle

Review maturity: Experimental

Security review declaration: Not declared

Lifecycle: Active

These release declarations are separate from evidence that a workflow operates in your environment.

Runtime & Dependencies

ID: marketing/lifecycle-experiment-planner

Tags: experimentation, ab-testing, lifecycle, statistics

Runtimes: codex, claude, generic

Tool dependencies: 1

API dependencies: 0

Dependencies

Tools: python3

APIs: Not documented

Outputs

  • Hypothesis plan
  • Sample-size guidance
  • Stop criteria