smithery/aiskillstore

growth-strategy

Design ethical growth strategies using product-led loops, measurable experiments, and privacy-aware guardrails.

Installation

$ npx skills add smithery/aiskillstore --skill growth-strategy

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Package contents

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  • skill md SKILL.md 4,857 B
  • docs SUMMARY.md 152 B

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SKILL.md

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 Type Example
Viral User creates → shares → new users
UGC/SEO User creates content → indexed → new users find
Paid Revenue → reinvest in ads → more revenue
Sales Customer → 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

Type Examples
Core NSM + input metrics
Guardrails Churn, 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.