growth-experimentation
Run systematic growth experiments across acquisition, activation, retention, and monetization with clear hypotheses and learning velocity. Use when building a growth practice, prioritizing tests, or accelerating learning under uncertainty.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- growth-experimentation
- description
- Run systematic growth experiments across acquisition, activation, retention, and monetization with clear hypotheses and learning velocity. Use when building a growth practice, prioritizing tests, or accelerating learning under uncertainty.
03 SKILL.md 正文
Growth Experimentation
Overview
Growth experimentation applies the scientific method to growth levers. The goal is not endless testing — it’s faster learning about what moves meaningful metrics.
When to Use
- Building or maturing a growth team practice
- Exploring new acquisition or activation levers
- Improving retention or monetization mechanically
- Reducing opinion-driven roadmap debates
Core Practices
- Maintain a prioritized experiment backlog
- Write hypotheses with metric, audience, and expected direction
- Design minimum viable tests
- Ensure measurement integrity before launch
- Share results openly, including failures
- Compound learnings into playbooks
Principles
- Learning velocity compounds into advantage
- Test the riskiest assumptions first
- Don’t A/B test what you can decide with logic or research
- Protect the user experience while experimenting
Verification
- [ ] Experiments have explicit hypotheses and success criteria
- [ ] Results are documented and shared
- [ ] Wins are productized; losses inform future bets
04 同仓库更多 Skills
compliance-monitoring
Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.
complaints-handling
Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.
aml-kyc-basics
Apply AML/KYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.
ai-model-compliance
Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
data-lineage
Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.