Refactor remaining skills for progressive disclosure

Phase 2 refactoring of skills >500 lines and medium-sized skills:

- paid-ads: 553 → 297 lines
  - Extract ad-copy-templates.md, audience-targeting.md, platform-setup-checklists.md

- analytics-tracking: 541 → 292 lines
  - Extract ga4-implementation.md, gtm-implementation.md, event-library.md

- ab-test-setup: 510 → 264 lines
  - Extract test-templates.md, sample-size-guide.md

- copywriting: 458 → 248 lines
  - Extract copy-frameworks.md (headline formulas, section types)

- page-cro: 336 → 180 lines
  - Extract experiments.md (experiment ideas by page type)

- onboarding-cro: 435 → 218 lines
  - Extract experiments.md (onboarding experiment ideas)

All skills now use progressive disclosure with references/ folders,
keeping SKILL.md files focused on core workflow while detailed
content is available when needed.

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

skills/paid-ads/references/audience-targeting.md

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+# Audience Targeting Reference
+
+Detailed targeting strategies for each major ad platform.
+
+## Google Ads Audiences
+
+### Search Campaign Targeting
+
+**Keywords:**
+- Exact match: [keyword] — most precise, lower volume
+- Phrase match: "keyword" — moderate precision and volume
+- Broad match: keyword — highest volume, use with smart bidding
+
+**Audience layering:**
+- Add audiences in "observation" mode first
+- Analyze performance by audience
+- Switch to "targeting" mode for high performers
+
+**RLSA (Remarketing Lists for Search Ads):**
+- Bid higher on past visitors searching your terms
+- Show different ads to returning searchers
+- Exclude converters from prospecting campaigns
+
+### Display/YouTube Targeting
+
+**Custom intent audiences:**
+- Based on recent search behavior
+- Create from your converting keywords
+- High intent, good for prospecting
+
+**In-market audiences:**
+- People actively researching solutions
+- Pre-built by Google
+- Layer with demographics for precision
+
+**Affinity audiences:**
+- Based on interests and habits
+- Better for awareness
+- Broad but can exclude irrelevant
+
+**Customer match:**
+- Upload email lists
+- Retarget existing customers
+- Create lookalikes from best customers
+
+**Similar/lookalike audiences:**
+- Based on your customer match lists
+- Expand reach while maintaining relevance
+- Best when source list is high-quality customers
+
+---
+
+## Meta Audiences
+
+### Core Audiences (Interest/Demographic)
+
+**Interest targeting tips:**
+- Layer interests with AND logic for precision
+- Use Audience Insights to research interests
+- Start broad, let algorithm optimize
+- Exclude existing customers always
+
+**Demographic targeting:**
+- Age and gender (if product-specific)
+- Location (down to zip/postal code)
+- Language
+- Education and work (limited data now)
+
+**Behavior targeting:**
+- Purchase behavior
+- Device usage
+- Travel patterns
+- Life events
+
+### Custom Audiences
+
+**Website visitors:**
+- All visitors (last 180 days max)
+- Specific page visitors
+- Time on site thresholds
+- Frequency (visited X times)
+
+**Customer list:**
+- Upload emails/phone numbers
+- Match rate typically 30-70%
+- Refresh regularly for accuracy
+
+**Engagement audiences:**
+- Video viewers (25%, 50%, 75%, 95%)
+- Page/profile engagers
+- Form openers
+- Instagram engagers
+
+**App activity:**
+- App installers
+- In-app events
+- Purchase events
+
+### Lookalike Audiences
+
+**Source audience quality matters:**
+- Use high-LTV customers, not all customers
+- Purchasers > leads > all visitors
+- Minimum 100 source users, ideally 1,000+
+
+**Size recommendations:**
+- 1% — most similar, smallest reach
+- 1-3% — good balance for most
+- 3-5% — broader, good for scale
+- 5-10% — very broad, awareness only
+
+**Layering strategies:**
+- Lookalike + interest = more precision early
+- Test lookalike-only as you scale
+- Exclude the source audience
+
+---
+
+## LinkedIn Audiences
+
+### Job-Based Targeting
+
+**Job titles:**
+- Be specific (CMO vs. "Marketing")
+- LinkedIn normalizes titles, but verify
+- Stack related titles
+- Exclude irrelevant titles
+
+**Job functions:**
+- Broader than titles
+- Combine with seniority level
+- Good for awareness campaigns
+
+**Seniority levels:**
+- Entry, Senior, Manager, Director, VP, CXO, Partner
+- Layer with function for precision
+
+**Skills:**
+- Self-reported, less reliable
+- Good for technical roles
+- Use as expansion layer
+
+### Company-Based Targeting
+
+**Company size:**
+- 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5000+
+- Key filter for B2B
+
+**Industry:**
+- Based on company classification
+- Can be broad, layer with other criteria
+
+**Company names (ABM):**
+- Upload target account list
+- Minimum 300 companies recommended
+- Match rate varies
+
+**Company growth rate:**
+- Hiring rapidly = budget available
+- Good signal for timing
+
+### High-Performing Combinations
+
+| Use Case | Targeting Combination |
+|----------|----------------------|
+| Enterprise sales | Company size 1000+ + VP/CXO + Industry |
+| SMB sales | Company size 11-200 + Manager/Director + Function |
+| Developer tools | Skills + Job function + Company type |
+| ABM campaigns | Company list + Decision-maker titles |
+| Broad awareness | Industry + Seniority + Geography |
+
+---
+
+## Twitter/X Audiences
+
+### Targeting options:
+- Follower lookalikes (accounts similar to followers of X)
+- Interest categories
+- Keywords (in tweets)
+- Conversation topics
+- Events
+- Tailored audiences (your lists)
+
+### Best practices:
+- Follower lookalikes of relevant accounts work well
+- Keyword targeting catches active conversations
+- Lower CPMs than LinkedIn/Meta
+- Less precise, better for awareness
+
+---
+
+## TikTok Audiences
+
+### Targeting options:
+- Demographics (age, gender, location)
+- Interests (TikTok's categories)
+- Behaviors (video interactions)
+- Device (iOS/Android, connection type)
+- Custom audiences (pixel, customer file)
+- Lookalike audiences
+
+### Best practices:
+- Younger skew (18-34 primarily)
+- Interest targeting is broad
+- Creative matters more than targeting
+- Let algorithm optimize with broad targeting
+
+---
+
+## Audience Size Guidelines
+
+| Platform | Minimum Recommended | Ideal Range |
+|----------|-------------------|-------------|
+| Google Search | 1,000+ searches/mo | 5,000-50,000 |
+| Google Display | 100,000+ | 500K-5M |
+| Meta | 100,000+ | 500K-10M |
+| LinkedIn | 50,000+ | 100K-500K |
+| Twitter/X | 50,000+ | 100K-1M |
+| TikTok | 100,000+ | 1M+ |
+
+Too narrow = expensive, slow learning
+Too broad = wasted spend, poor relevance
+
+---
+
+## Exclusion Strategy
+
+Always exclude:
+- Existing customers (unless upsell)
+- Recent converters (7-14 days)
+- Bounced visitors (<10 sec)
+- Employees (by company or email list)
+- Irrelevant page visitors (careers, support)
+- Competitors (if identifiable)