growth-marketer
Growth marketing covering experimentation, funnel optimization, acquisition channels, retention, and viral growth. Use when designing A/B experiments, optimizing AARRR funnel stages, or prioritizing channels by CAC and LTV.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/borghei/claude-skills --skill growth-marketer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads growth-marketer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Growth Marketer
The agent operates as a senior growth marketer, delivering experiment-driven strategies for scalable user acquisition, activation, retention, referral, and revenue optimization.
Clarify First
Before designing experiments or a growth plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- North Star Metric + current AARRR baselines — the metric and per-stage numbers (Steps 1–2 require these; without a baseline the "biggest lever" is a guess)
- Experiment hypothesis + primary/guardrail metrics — the change expected and how it's judged (drives the experiment doc and ship/iterate/kill)
- Baseline rate + MDE — current conversion and smallest lift worth detecting (feeds the sample-size calc directly)
- Daily eligible traffic — visitors per variant per day (determines whether the test can reach significance and over what duration)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Workflow
- Define North Star Metric - Identify the single metric that reflects customer value and leads to revenue. Checkpoint: the metric must be measurable, actionable, and correlated with retention.
- Map the AARRR funnel - Quantify current performance at each stage (Acquisition, Activation, Retention, Referral, Revenue). Checkpoint: every stage has a baseline number and a target.
- Identify biggest lever - Find the funnel stage with the largest drop-off or lowest performance vs. benchmark. This becomes the focus area.
- Design experiments - Write hypotheses using the format: "If we [change], then [metric] will [direction] by [amount] because [reasoning]." Prioritize using ICE scoring.
- Calculate sample size and run - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment.
- Analyze results - Evaluate lift, p-value, and guardrail metrics. Decision: Ship, Iterate, or Kill.
- Model growth trajectory - Forecast user growth incorporating acquisition rate, churn, and viral coefficient. Validate that LTV:CAC > 3:1 for sustainability.
AARRR Funnel (Pirate Metrics)
| Stage | Key Question | Metrics | Benchmark |
|---|---|---|---|
| Acquisition | How do users find us? | Traffic, CAC, channel mix | CAC < 1/3 LTV |
| Activation | Great first experience? | Activation rate, time to value | 40%+ activation |
| Retention | Do users come back? | D1/D7/D30 retention, churn | SaaS: D30 30% |
| Referral | Do users tell others? | Viral coefficient (K), NPS | K-factor > 0.5 |
| Revenue | How do we monetize? | ARPU, LTV, conversion rate | LTV:CAC > 3:1 |
Experimentation Framework
Experiment Document Template
# Experiment: Onboarding Checklist v2
## Hypothesis
If we add a progress bar to the onboarding checklist, then activation rate
will increase by 15% because users respond to completion motivation.
## Metrics
- Primary: 7-day activation rate
- Secondary: Time to first value action
- Guardrails: Support ticket volume, bounce rate
## Design
- Type: A/B test
- Sample: 8,200 per variant (5% baseline, 15% MDE, 95% confidence)
- Duration: 14 days
- Segments: New signups only
## Results
| Variant | Users | Activation | Lift | p-value |
|-----------|--------|------------|-------|---------|
| Control | 8,350 | 5.1% | - | - |
| Treatment | 8,280 | 6.2% | +21% | 0.003 |
## Decision: Ship
ICE Prioritization
| Experiment | Impact (1-10) | Confidence (1-10) | Ease (1-10) | ICE Score |
|---|---|---|---|---|
| Onboarding checklist v2 | 8 | 7 | 9 | 24 |
| Referral incentive test | 6 | 8 | 7 | 21 |
| Pricing page redesign | 9 | 5 | 6 | 20 |
Sample Size Calculator
from scipy import stats
def sample_size(baseline_rate, mde, alpha=0.05, power=0.8):
"""Calculate required sample size per variant for an A/B test.
Args:
baseline_rate: Current conversion rate (e.g. 0.05 for 5%)
mde: Minimum detectable effect as proportion (e.g. 0.15 for 15% lift)
alpha: Significance level (default 0.05)
power: Statistical power (default 0.8)
Returns:
Required users per variant (int)
Example:
>>> sample_size(0.05, 0.15)
8218
"""
effect_size = mde * baseline_rate
z_alpha = stats.norm.ppf(1 - alpha / 2)
z_beta = stats.norm.ppf(power)
n = 2 * ((z_alpha + z_beta) ** 2) * baseline_rate * (1 - baseline_rate) / (effect_size ** 2)
return int(n)
Acquisition Channel Analysis
| Channel | CAC | Volume | Quality | Scalability |
|---|---|---|---|---|
| Organic Search | $20 | High | High | Medium |
| Paid Search | $50 | Medium | High | High |
| Social Organic | $10 | Medium | Medium | Low |
| Social Paid | $40 | High | Medium | High |
| Content | $15 | Medium | High | Medium |
| Referral | $5 | Low | Very High | Medium |
| Partnerships | $30 | Medium | High | Medium |
Retention Benchmarks
| Category | D1 | D7 | D30 |
|---|---|---|---|
| SaaS | 60% | 40% | 30% |
| Social | 50% | 30% | 20% |
| E-commerce | 25% | 15% | 10% |
| Games | 35% | 15% | 8% |
Cohort Analysis Example
Week 0 Week 1 Week 2 Week 3 Week 4
Jan W1 100% 45% 35% 28% 25%
Jan W2 100% 48% 38% 32% 28%
Jan W3 100% 52% 42% 35% 31%
Jan W4 100% 55% 45% 38% 34%
Insight: Week-over-week improvement correlates with onboarding
changes shipped in Jan W3.
Viral Growth
K-Factor = invites per user (i) x conversion rate of invites (c)
- K > 1: True viral growth (each user brings >1 new user)
- K = 0.5-1: Viral boost (amplifies paid acquisition)
- K < 0.5: Minimal viral effect
Growth Forecast Model
def growth_forecast(current_users, monthly_growth_rate, months):
"""Forecast user base over time with compound growth.
Example:
>>> growth_forecast(10000, 0.10, 12)[-1]
31384
"""
users = [current_users]
for _ in range(months):
users.append(int(users[-1] * (1 + monthly_growth_rate)))
return users
Troubleshooting
| Symptom | Likely Cause | Resolution |
|---|---|---|
| K-factor below 0.1 despite referral program | Invite UX has too much friction or incentive misaligned with user value | Reduce invite flow to one click; align incentive with product value (usage credits > cash) |
| Activation rate below 20% for new signups | Time-to-value too long or onboarding not guiding users to aha moment | Map activation events, identify first value action, build guided onboarding to reach it in under 5 minutes |
| Growth stalls after initial PLG ramp | Free tier captures low-intent users who never convert; paid conversion rate below 3% | Tighten free tier limits around high-value features, add contextual upgrade prompts at usage gates |
| A/B test results not reaching significance | Sample size too small for the minimum detectable effect being tested | Use sample size calculator; increase traffic to test or accept larger MDE |
| Cohort retention curves flatten at under 15% | Product does not build enough habit; no ongoing value loop | Implement engagement hooks (notifications, reports, streaks); investigate which features drive retention |
| Experiments consistently show no lift | Testing cosmetic changes rather than meaningful value propositions | Focus experiments on activation flow, pricing, and value communication — not button colors |
Success Criteria
- North Star Metric identified, measurable, and reviewed weekly with cross-functional team
- Activation rate above 40% for new signups within first 7 days
- LTV:CAC ratio sustained above 3:1 across all acquisition channels
- K-factor above 0.5, providing meaningful viral amplification of paid acquisition
- Experiment velocity of 2+ tests per sprint with documented hypotheses and outcomes
- D30 retention at or above SaaS benchmark (30%) for primary user segment
- Growth model accurately forecasts within 15% of actual for 3-month projections
Scope & Limitations
In Scope: AARRR funnel optimization, experiment design and prioritization (ICE/RICE), viral growth modeling, PLG strategy, retention analysis, cohort analysis, growth forecasting, acquisition channel analysis, sample size calculation.
Out of Scope: Brand strategy (see brand-strategist skill), content creation (see content-creator skill), paid ad campaign management (see paid-ads skill), product design and engineering implementation, pricing strategy.
Limitations: Growth loop models use simplified compound growth assumptions — real growth has diminishing returns and market saturation effects. Viral coefficient calculations assume uniform user behavior; actual viral spread varies by segment. Sample size calculator uses normal approximation; for very low conversion rates, exact tests may be needed.
Scripts
| Script | Purpose | Usage |
|---|---|---|
scripts/growth_loop_modeler.py | Model viral, PLG, and content growth loops with forecasts | python scripts/growth_loop_modeler.py --type viral --users 1000 --k-factor 0.6 --months 12 |
scripts/viral_coefficient_calculator.py | Calculate K-factor, branching factor, and improvement scenarios | python scripts/viral_coefficient_calculator.py --invites 5000 --conversions 800 --users 2000 |
scripts/experiment_prioritizer.py | Prioritize growth experiments using ICE or RICE scoring | python scripts/experiment_prioritizer.py experiments.json --framework ice --demo |
FAQ
What does the growth-marketer skill do?
Growth marketing covering experimentation, funnel optimization, acquisition channels, retention, and viral growth. Use when designing A/B experiments, optimizing AARRR funnel stages, or prioritizing channels by CAC and LTV.
How do I install growth-marketer?
Run npx skills add https://github.com/borghei/claude-skills --skill growth-marketer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/growth-marketer/ (personal) or .claude/skills/growth-marketer/ (project).
Which AI agents can use growth-marketer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is growth-marketer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains growth-marketer?
It is published by borghei in borghei/claude-skills. It has 428 recorded installs.
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