Optimize skill files for AI agent use with progressive disclosure

- Fix marketplace.json: add 2 missing skills (content-strategy, product-marketing-context)
- Refactor 10 skills over 500 lines to use references/ folders:
  - email-sequence: 926 → 291 lines
  - social-content: 809 → 276 lines
  - competitor-alternatives: 750 → 253 lines
  - pricing-strategy: 712 → 226 lines
  - programmatic-seo: 628 → 235 lines
  - referral-program: 604 → 239 lines
  - schema-markup: 598 → 175 lines
  - free-tool-strategy: 576 → 176 lines
  - paywall-upgrade-cro: 572 → 224 lines
  - marketing-ideas: 566 → 165 lines

Each skill now has core workflow in SKILL.md (<500 lines) with detailed
content in references/ folder for progressive disclosure.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

skills/referral-program/references/program-examples.md

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+# Referral Program Examples
+
+Real-world examples of successful referral programs.
+
+## Dropbox (Classic)
+
+**Program:** Give 500MB storage, get 500MB storage
+
+**Why it worked:**
+- Reward directly tied to product value
+- Low friction (just an email)
+- Both parties benefit equally
+- Gamified with progress tracking
+
+---
+
+## Uber/Lyft
+
+**Program:** Give $10 ride credit, get $10 when they ride
+
+**Why it worked:**
+- Immediate, clear value
+- Double-sided incentive
+- Easy to share (code/link)
+- Triggered at natural moments
+
+---
+
+## Morning Brew
+
+**Program:** Tiered rewards for subscriber referrals
+- 3 referrals: Newsletter stickers
+- 5 referrals: T-shirt
+- 10 referrals: Mug
+- 25 referrals: Hoodie
+
+**Why it worked:**
+- Gamification drives ongoing engagement
+- Physical rewards are shareable (more referrals)
+- Low cost relative to subscriber value
+- Built status/identity
+
+---
+
+## Notion
+
+**Program:** $10 credit per referral (education)
+
+**Why it worked:**
+- Targeted high-sharing audience (students)
+- Product naturally spreads in teams
+- Credit keeps users engaged
+
+---
+
+## Incentive Types Comparison
+
+| Type | Pros | Cons | Best For |
+|------|------|------|----------|
+| Cash/credit | Universally valued | Feels transactional | Marketplaces, fintech |
+| Product credit | Drives usage | Only valuable if they'll use it | SaaS, subscriptions |
+| Free months | Clear value | May attract freebie-seekers | Subscription products |
+| Feature unlock | Low cost to you | Only works for gated features | Freemium products |
+| Swag/gifts | Memorable, shareable | Logistics complexity | Brand-focused companies |
+| Charity donation | Feel-good | Lower personal motivation | Mission-driven brands |
+
+---
+
+## Incentive Sizing Framework
+
+**Calculate your maximum incentive:**
+```
+Max Referral Reward = (Customer LTV × Gross Margin) - Target CAC
+```
+
+**Example:**
+- LTV: $1,200
+- Gross margin: 70%
+- Target CAC: $200
+- Max reward: ($1,200 × 0.70) - $200 = $640
+
+**Typical referral rewards:**
+- B2C: $10-50 or 10-25% of first purchase
+- B2B SaaS: $50-500 or 1-3 months free
+- Enterprise: Higher, often custom
+
+---
+
+## Viral Coefficient & Metrics
+
+### Key Metrics
+
+**Viral coefficient (K-factor):**
+```
+K = Invitations × Conversion Rate
+
+K > 1 = Viral growth (each user brings more than 1 new user)
+K < 1 = Amplified growth (referrals supplement other acquisition)
+```
+
+**Example:**
+- Average customer sends 3 invitations
+- 15% of invitations convert
+- K = 3 × 0.15 = 0.45
+
+**Referral rate:**
+```
+Referral Rate = (Customers who refer) / (Total customers)
+```
+
+Benchmarks:
+- Good: 10-25% of customers refer
+- Great: 25-50%
+- Exceptional: 50%+
+
+**Referrals per referrer:**
+
+Benchmarks:
+- Average: 1-2 referrals per referrer
+- Good: 2-5
+- Exceptional: 5+
+
+### Calculating Referral Program ROI
+
+```
+Referral Program ROI = (Revenue from referred customers - Program costs) / Program costs
+
+Program costs = Rewards paid + Tool costs + Management time
+```
+
+**Track separately:**
+- Cost per referred customer (CAC via referral)
+- LTV of referred customers (often higher than average)
+- Payback period for referral rewards