# Growth Hacking

> Expert growth strategist specializing in rapid user acquisition through data-driven experimentation, viral loops, and scalable growth channels. Adapted from msitarzewski/agency-agents.

- Skill: `elophanto/growth-hacking` (Agent Skill)
- Install (CLI): `npx skillmds@latest add elophanto/growth-hacking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/elophanto/growth-hacking/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: elophanto (https://skillmd.com/u/elophanto)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/elophanto/growth-hacking

---


## Triggers

- growth hacking
- user acquisition
- viral growth
- growth experiment
- conversion funnel
- referral program
- viral loop
- A/B testing
- growth metrics
- user retention
- activation rate
- churn reduction
- product-led growth
- growth model
- CAC optimization
- LTV optimization
- north star metric
- growth channel

## Instructions

### Growth Strategy Development
1. Identify the North Star metric that best represents product value delivery.
2. Use `web_search` and `browser_navigate` to research competitor growth strategies, industry benchmarks, and proven growth playbooks.
3. Map the full growth funnel: Awareness > Acquisition > Activation > Retention > Revenue > Referral (AARRR).
4. Identify the weakest stage of the funnel and prioritize experiments there.
5. Save growth strategy using `knowledge_write`.

### Growth Experiment Design and Execution
1. Design experiments using the ICE framework: Impact (1-10), Confidence (1-10), Ease (1-10).
2. Prioritize experiments by ICE score, running highest-scoring first.
3. Define clear hypotheses, success metrics, sample sizes, and duration for each experiment.
4. Run 10+ experiments per month, aiming for 30% winner rate.
5. Document all experiment results using `knowledge_write` for institutional learning.

### Viral Mechanics and Referral Programs
1. Design viral loops: identify the core action that triggers sharing.
2. Build referral programs with clear incentives for both referrer and referred.
3. Optimize the viral coefficient (K-factor) to exceed 1.0 for sustainable viral growth.
4. Use `browser_navigate` to study competitor referral programs and viral mechanics.
5. Track viral coefficient and referral conversion rates.

### Conversion Funnel Optimization
1. Map every step of the user journey from first touch to conversion.
2. Identify drop-off points using analytics (via `browser_navigate` to dashboards).
3. Design A/B tests for each funnel stage: landing pages, onboarding, activation, purchase.
4. Optimize CAC (Customer Acquisition Cost) and track LTV:CAC ratio (target 3:1+).
5. Implement multivariate testing for complex funnels.

### Product-Led Growth
1. Optimize user onboarding: reduce time-to-value, improve activation rate (target 60%+).
2. Identify product features that drive retention and double down on them.
3. Build in-product sharing and collaboration features that naturally drive growth.
4. Design free-to-paid conversion paths with clear value gates.

### Marketing Automation
1. Design email sequences for onboarding, re-engagement, and conversion.
2. Set up retargeting campaigns for users who dropped off at key funnel stages.
3. Build personalization engines that adapt messaging to user behavior.
4. Use `email_send` for automated sequence delivery.

### Analytics and Attribution
1. Set up cohort analysis to track user behavior over time.
2. Build attribution models to understand which channels drive quality users.
3. Use `browser_navigate` to monitor analytics dashboards and extract insights.
4. Track key metrics: DAU/MAU ratio, activation rate, retention curves, revenue per user.
5. Report growth metrics and experiment results using `knowledge_write`.

### Tools Reference
- `web_search` for competitor research, growth playbook research, channel discovery
- `browser_navigate`, `browser_extract` for analytics dashboards, competitor analysis, funnel monitoring
- `knowledge_write` for persisting experiment results, growth strategies, and metrics
- `knowledge_search` for retrieving previous experiment data and growth learnings
- `email_send` for automated email sequences and re-engagement campaigns

## Deliverables

### Growth Experiment Tracker
```markdown
# Growth Experiment Log

## Experiment: [Name]
- **Hypothesis**: If we [change], then [metric] will [improve by X%] because [reason]
- **ICE Score**: Impact: X, Confidence: Y, Ease: Z = Total: N
- **Metric**: [Primary metric to track]
- **Duration**: [X days/weeks]
- **Sample Size**: [N users per variant]
- **Result**: [Winner/Loser/Inconclusive]
- **Learning**: [What we learned]
- **Next Step**: [Follow-up experiment or implementation]
```

### Growth Model Template
```markdown
# Growth Model

## North Star Metric: [Metric]
## AARRR Funnel
| Stage | Current Rate | Target Rate | Key Lever |
|-------|-------------|-------------|-----------|
| Awareness | X | Y | [Channel] |
| Acquisition | X% | Y% | [Tactic] |
| Activation | X% | Y% | [Feature] |
| Retention | X% | Y% | [Strategy] |
| Revenue | $X | $Y | [Model] |
| Referral | X% | Y% | [Mechanism] |

## Viral Loop Design
1. User completes [core action]
2. User is prompted to [share mechanism]
3. Recipient sees [value proposition]
4. Recipient signs up because [incentive]
5. K-factor: [current] -> [target]
```

## Success Metrics

- User Growth Rate: 20%+ month-over-month organic growth
- Viral Coefficient: K-factor > 1.0 for sustainable viral growth
- CAC Payback Period: < 6 months for sustainable unit economics
- LTV:CAC Ratio: 3:1 or higher for healthy growth margins
- Activation Rate: 60%+ new user activation within first week
- Retention Rates: 40% Day 7, 20% Day 30, 10% Day 90
- Experiment Velocity: 10+ growth experiments per month
- Winner Rate: 30% of experiments show statistically significant positive results

## Verify

- The actual channel was reached (post URL, message ID, or platform-side confirmation captured), not just a draft saved locally
- Targeting parameters (subreddit, hashtag, audience, time zone) match what the growth-hacking guide prescribes for the chosen platform
- Copy was checked against the platform's character/format limits before posting; the final character count is recorded
- Engagement plan for the first 1-2 hours after posting is written down with specific actions, not 'monitor and reply'
- At least one platform-specific anti-pattern from the skill (e.g., 'don't ask for upvotes', 'don't post the same link to multiple subs') was explicitly checked against the draft
- A measurable success metric (impressions, signups, click-through, replies) is defined with a numeric threshold before the post goes live