GR

Framework for building growth loops and product-led acquisition strategies.

Install

mkdir -p .claude/skills/growth-strategy && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/5123" && unzip -o skill.zip -d .claude/skills/growth-strategy && rm skill.zip

Installs to .claude/skills/growth-strategy

Activation

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Designing growth strategy or GTM plans - Planning experiments and A/B tests - Optimizing activation, retention, or referral flows
129 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Defines North Star metrics and input metrics
  • Categorizes growth loops vs linear funnels
  • Sets up experimentation intake systems
  • Defines activation milestones and cohort tracking
  • Assesses tactics for incrementality and guardrails

How it works

It applies a framework-based checklist to audit whether a growth tactic qualifies as a compounding loop or just noise.

Inputs & outputs

You give it
Growth objective or GTM plan detail
You get back
Structured experiment design or loop model

When to use growth-strategy

  • Planning product-led growth experiments
  • Designing viral referral loops
  • Defining event taxonomy for activation milestones

About this skill

Growth Strategy

Modern growth hacking: loops + product-led growth + disciplined experimentation, under privacy and deliverability constraints.

When to Use

  • Designing growth strategy or GTM plans
  • Planning experiments and A/B tests
  • Optimizing activation, retention, or referral flows
  • Building viral/referral loops
  • Reviewing growth tactics for ethics/compliance

Core Principle

If a "hack" doesn't strengthen a loop or an input metric, it's noise.

1. Growth Model First

North Star Metric (NSM)

  • Single metric aligning the whole org
  • Plus input metrics (leading indicators you can move weekly)
  • Avoid vanity metrics

Growth Loops > Funnels

  • Loops: Closed systems where outputs feed inputs → compounding growth
  • Funnels: Linear → diminishing returns

Common loops:

Loop TypeExample
ViralUser creates → shares → new users
UGC/SEOUser creates content → indexed → new users find
PaidRevenue → reinvest in ads → more revenue
SalesCustomer → case study → new leads

Product-Led Growth (B2B/SaaS)

Product itself drives: Acquisition → Activation → Retention → Monetization

2. Instrumentation

Event Taxonomy

  • Clean identity resolution: anonymous → user → account
  • Cohort retention tracking
  • Activation milestones defined

Incrementality

  • Holdouts / geo splits when attribution is noisy
  • Don't trust last-click blindly

Metric Categories

TypeExamples
CoreNSM + input metrics
GuardrailsChurn, spam rate, refunds, latency, NPS

3. Experimentation Engine

Intake System

  • Single queue + scoring (RICE/ICE)
  • Weekly cadence

Test Definition (Required)

  • Hypothesis
  • Target segment
  • Success metric
  • Guardrail metrics
  • Sample size rule
  • Kill criteria

High-ROI Test Areas

  • Onboarding steps
  • Paywall copy
  • Pricing/packaging
  • Referral incentive
  • Landing page variants
  • Lifecycle messages

4. Lever-Specific Playbooks

Activation & Onboarding (Highest ROI)

  • Reduce time-to-value
  • Templates, importers, "one-click first win"
  • Progressive disclosure (ask when needed, not upfront)
  • Guided setup flows

Viral/Referral Loops

  • Build shareable artifacts (reports, badges, embeds)
  • "Invite teammates" as natural workflow
  • Reward activated referrals, not just signups

Content + SEO

  • Programmatic SEO: template + real value + strong linking
  • Audit/prune thin pages (don't endlessly generate)
  • Quality > quantity

Lifecycle (Email/Push)

Deliverability is gating factor:

  • SPF/DKIM for all senders
  • DMARC for bulk
  • Keep complaint/spam rates low

Community-Led Growth

  • Seed right early members
  • Great "first experience"
  • Connect to business outcomes (support deflection, referrals)

5. Privacy & Measurement Constraints

Expect

  • Less reliable cross-site tracking
  • Cookie-based attribution unstable
  • Platform policy changes

Adapt

  • First-party data focus
  • Server-side signals
  • Incrementality testing
  • Design measurement that survives policy changes

6. AI in Growth

Good Uses

  • Generate creative/landing page variants to test (humans review)
  • Summarize qualitative feedback
  • Cluster objections
  • Speed up research

Avoid

  • "AI content spam" at scale without quality control
  • Backfires in SEO and brand

7. Hard Red Lines

If a tactic can't survive being in a postmortem or public doc, don't ship it.

Never:

  • Spam (email/SMS)
  • Fake reviews
  • Scraping that violates ToS
  • Dark patterns
  • Deceptive pricing/consent

Output Format

When proposing growth initiatives:

## Initiative: [Name]
**Loop/Lever**: [Which growth loop or lever this strengthens]
**Hypothesis**: [If we do X, Y metric will improve by Z because...]
**Input Metric**: [What leading indicator we're moving]
**Guardrails**: [Metrics that must not regress]

### Implementation
[Concrete steps]

### Measurement
[How we'll know it worked]

### Kill Criteria
[When to stop if failing]

Quick Checklist

Before shipping any growth tactic:

  • Does it strengthen a loop or input metric?
  • Is the hypothesis testable?
  • Are guardrails defined?
  • Is it compliant with platform ToS?
  • Would you put it in a public doc?
  • Does it respect user privacy?
  • Is deliverability accounted for (if email)?

See references/ for detailed playbooks.

When not to use it

  • For non-growth-oriented projects
  • When data tracking infrastructure is non-existent

Limitations

  • Subjective evaluation of loop quality
  • Dependent on organizational buy-in for tracking

How it compares

It shifts focus from tactical 'hacks' to compounding systems and leading indicators.

Compared to similar skills

growth-strategy side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
growth-strategy (this skill)16moNo flagsIntermediate
product-strategist127moReviewAdvanced
mckinsey-consultant94moNo flagsAdvanced
scenario-war-room12moReviewAdvanced

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