# Build an Ethical Growth Strategy

Growth work can drift toward vanity metrics, unreliable attribution, and tactics that harm user trust. This skill turns product goals into measurable growth loops, controlled experiments, and privacy-aware guardrails.

## Install

```bash
npx skillstore add clementwalter/growth-strategy
```

## Metadata

- Status: approved
- Slug: clementwalter-growth-strategy
- Skillstore revision: r2
- Version status: missing
- Tree hash: 097b3d62639ade74651b6b3ca7e76bc5e9b99488385be8599d88805f91087c4c
- Author: ClementWalter
- GitHub username: ClementWalter
- License: MIT
- Repository: https://github.com/ClementWalter/rookie-marketplace/tree/main/growth-hacker/skills/growth-strategy
- Ref: c68df504887c54bf71d2d467a1fbebde49467868
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: confirmation\_required
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Quality score: 69
- Public page: https://skillstore.pages.dev/skills/clementwalter-growth-strategy
- Manifest: https://skillstore.pages.dev/api/skills/clementwalter-growth-strategy/manifest

## Capabilities

- Frames initiatives around a north star metric and leading input metrics.
- Designs growth loops for viral, content, paid, and sales-led acquisition.
- Creates experiment plans with hypotheses, segments, success metrics, guardrails, and kill criteria.
- Suggests improvements for activation, onboarding, retention, referrals, and lifecycle messaging.
- Recommends measurement approaches such as cohorts, holdouts, and incrementality testing.
- Screens proposed tactics against privacy, deliverability, platform policy, and ethical constraints.

## Use Cases

- Plan a SaaS activation experiment: Define an onboarding test that reduces time-to-value while protecting retention and support metrics.
- Improve referral growth responsibly: Design a referral loop that rewards activated users and avoids spam or deceptive incentives.
- Review a measurement plan: Assess attribution assumptions and add cohort, holdout, or incrementality methods where needed.

## Prompt Templates

### Find a growth focus

```
Our product is [product] for [audience]. Our goal is [goal]. Suggest one north star metric, three input metrics, and the first growth lever to investigate.
```

### Design an onboarding test

```
Create an experiment for [product] onboarding. Include the hypothesis, target segment, success metric, guardrails, sample-size approach, implementation steps, and kill criteria.
```

### Build a referral loop

```
Design an ethical referral loop for [product and audience]. Explain the user value, sharing trigger, reward rule, activation definition, abuse safeguards, and measurements.
```

### Prioritize a growth portfolio

```
Given these opportunities: [list], score them with RICE or ICE. Recommend a four-week experiment portfolio with owners, dependencies, metrics, guardrails, and stop conditions.
```

## Limitations

- It does not access analytics platforms or validate live business data.
- It cannot guarantee experiment results or forecast precise growth outcomes.
- It does not replace legal, privacy, deliverability, or platform-policy review.
- Recommendations require product context, implementation resources, and human judgment.

## Best Practices

- Start with a clear business goal, north star metric, and leading input metrics.
- Define guardrails and kill criteria before launching an experiment.
- Use first-party data and test incrementality when attribution is uncertain.

## Anti Patterns

- Do not optimize vanity metrics without checking retention, quality, and user value.
- Do not send unsolicited messages, use fake reviews, or use deceptive incentives.
- Do not scale AI-generated content without quality review and a clear user benefit.

## Security Audit

- Audited at: 2026-07-18T10:47:04.416\+00:00
- Summary: Manual review of SKILL.md found a prompt-only growth strategy guide with no executable instructions, data collection, or prompt-injection language. The static metadata finding resulted from invalid frontmatter and is a false positive for security risk, though valid frontmatter would prevent future scanning gaps.

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