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/pricing-strategy/references/research-methods.md

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+# Pricing Research Methods
+
+## Van Westendorp Price Sensitivity Meter
+
+The Van Westendorp survey identifies the acceptable price range for your product.
+
+### The Four Questions
+
+Ask each respondent:
+1. "At what price would you consider [product] to be so expensive that you would not consider buying it?" (Too expensive)
+2. "At what price would you consider [product] to be priced so low that you would question its quality?" (Too cheap)
+3. "At what price would you consider [product] to be starting to get expensive, but you still might consider it?" (Expensive/high side)
+4. "At what price would you consider [product] to be a bargain—a great buy for the money?" (Cheap/good value)
+
+### How to Analyze
+
+1. Plot cumulative distributions for each question
+2. Find the intersections:
+   - **Point of Marginal Cheapness (PMC):** "Too cheap" crosses "Expensive"
+   - **Point of Marginal Expensiveness (PME):** "Too expensive" crosses "Cheap"
+   - **Optimal Price Point (OPP):** "Too cheap" crosses "Too expensive"
+   - **Indifference Price Point (IDP):** "Expensive" crosses "Cheap"
+
+**The acceptable price range:** PMC to PME
+**Optimal pricing zone:** Between OPP and IDP
+
+### Survey Tips
+- Need 100-300 respondents for reliable data
+- Segment by persona (different willingness to pay)
+- Use realistic product descriptions
+- Consider adding purchase intent questions
+
+### Sample Output
+
+```
+Price Sensitivity Analysis Results:
+─────────────────────────────────
+Point of Marginal Cheapness:  $29/mo
+Optimal Price Point:          $49/mo
+Indifference Price Point:     $59/mo
+Point of Marginal Expensiveness: $79/mo
+
+Recommended range: $49-59/mo
+Current price: $39/mo (below optimal)
+Opportunity: 25-50% price increase without significant demand impact
+```
+
+---
+
+## MaxDiff Analysis (Best-Worst Scaling)
+
+MaxDiff identifies which features customers value most, informing packaging decisions.
+
+### How It Works
+
+1. List 8-15 features you could include
+2. Show respondents sets of 4-5 features at a time
+3. Ask: "Which is MOST important? Which is LEAST important?"
+4. Repeat across multiple sets until all features compared
+5. Statistical analysis produces importance scores
+
+### Example Survey Question
+
+```
+Which feature is MOST important to you?
+Which feature is LEAST important to you?
+
+□ Unlimited projects
+□ Custom branding
+□ Priority support
+□ API access
+□ Advanced analytics
+```
+
+### Analyzing Results
+
+Features are ranked by utility score:
+- High utility = Must-have (include in base tier)
+- Medium utility = Differentiator (use for tier separation)
+- Low utility = Nice-to-have (premium tier or cut)
+
+### Using MaxDiff for Packaging
+
+| Utility Score | Packaging Decision |
+|---------------|-------------------|
+| Top 20% | Include in all tiers (table stakes) |
+| 20-50% | Use to differentiate tiers |
+| 50-80% | Higher tiers only |
+| Bottom 20% | Consider cutting or premium add-on |
+
+---
+
+## Willingness to Pay Surveys
+
+**Direct method (simple but biased):**
+"How much would you pay for [product]?"
+
+**Better: Gabor-Granger method:**
+"Would you buy [product] at [$X]?" (Yes/No)
+Vary price across respondents to build demand curve.
+
+**Even better: Conjoint analysis:**
+Show product bundles at different prices
+Respondents choose preferred option
+Statistical analysis reveals price sensitivity per feature
+
+---
+
+## Usage-Value Correlation Analysis
+
+### 1. Instrument usage data
+Track how customers use your product:
+- Feature usage frequency
+- Volume metrics (users, records, API calls)
+- Outcome metrics (revenue generated, time saved)
+
+### 2. Correlate with customer success
+- Which usage patterns predict retention?
+- Which usage patterns predict expansion?
+- Which customers pay the most, and why?
+
+### 3. Identify value thresholds
+- At what usage level do customers "get it"?
+- At what usage level do they expand?
+- At what usage level should price increase?
+
+### Example Analysis
+
+```
+Usage-Value Correlation Analysis:
+─────────────────────────────────
+Segment: High-LTV customers (>$10k ARR)
+Average monthly active users: 15
+Average projects: 8
+Average integrations: 4
+
+Segment: Churned customers
+Average monthly active users: 3
+Average projects: 2
+Average integrations: 0
+
+Insight: Value correlates with team adoption (users)
+        and depth of use (integrations)
+
+Recommendation: Price per user, gate integrations to higher tiers
+```