feat(skills): add marketing skills collection

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+# Pricing Models
+
+The eight core ways to structure *how* you charge. This is distinct from the value metric (what unit you charge on) and the tier structure (how you package). Most real products **combine** two or more of these.
+
+## Contents
+- The 8 Pricing Models
+- Combining Models
+- The Value/Price Ratio
+- The Low-Price Retention Counterpoint
+
+---
+
+## The 8 Pricing Models
+
+| Model | How it works | Best when | Reference |
+|-------|-------------|-----------|-----------|
+| **Flat-rate** | One price, one product, everyone pays the same | Simple product, one persona, you want zero pricing friction | Basecamp |
+| **Usage-based** | Pay for what you consume (metered) | Value scales directly with volume; consumption is variable and easy to meter | Stripe |
+| **Tier-based** | Good-better-best packages at set prices | Distinct segments with different needs and budgets | Kinsta |
+| **User-based** | Price per seat/user | Value grows as more people in the org use it (collaboration) | Notion |
+| **Feature-based** | Price gated by which capabilities are unlocked | Clear feature tiers map to willingness to pay | Intercom |
+| **Credit-based** | Buy a bucket of credits, spend them on actions | Usage is lumpy or bursty; you want prepaid commitment and simple mental accounting | Audible |
+| **Outcome-based** | Pay per result delivered (resolution, task completed) | You can measure and attribute the outcome, and the outcome is what the buyer actually wants | Intercom Fin, Zapier |
+| **Hybrid** | Deliberate mix (e.g. platform fee + usage, or seats + credits) | A single model under- or over-charges different customers | Drift |
+
+### When to reach for each
+
+- **Flat-rate** — reach for it first if you can. It's the easiest to sell, easiest to understand, easiest to forecast. The tradeoff: you leave money on the table with your biggest customers.
+- **Usage-based** — the fairest model when consumption tracks value, but revenue is less predictable and buyers fear a surprise bill. Pair with spend caps or alerts.
+- **Tier-based** — the default for self-serve SaaS. Lets one page serve SMB through mid-market.
+- **User-based** — only if value genuinely rises with headcount. If it doesn't, seats punish adoption (teams share logins to avoid paying).
+- **Feature-based** — powerful for segmentation, but don't gate the feature that delivers your core value; gate the ones that separate casual from serious users.
+- **Credit-based** — good for AI/actions-based products where each action has a cost. Credits decouple price from a single unit and make prepayment feel natural.
+- **Outcome-based** — the emerging model for AI agents (charge per resolved ticket, per automation run). Highest trust because the buyer only pays when they win — but only viable when the outcome is measurable and clearly attributable to you.
+- **Hybrid** — where most mature products end up. A base platform fee for predictability plus a usage/outcome component for upside.
+
+---
+
+## Combining Models
+
+These aren't mutually exclusive. Common combinations:
+
+- **Tiers + per-user** — seats within each package (most B2B SaaS)
+- **Platform fee + usage** — predictable base, variable upside (Twilio-style)
+- **Seats + credits** — pay per person, then top up credits for heavy actions
+- **Feature tiers + outcome** — unlock capabilities by tier, charge per result on top
+
+Pick the primary model from the value metric, then layer a second only if a single model clearly mis-prices a real segment.
+
+---
+
+## The Value/Price Ratio
+
+Aim for roughly a **10:1 value-to-price ratio** (Ryan Kulp): the customer should perceive about **10x more value than they pay**. This is the buffer that makes the purchase feel obvious rather than negotiated, and it leaves headroom to raise prices later as you add value.
+
+If you can't articulate 10x value, the problem is usually the offer or the positioning, not the price point.
+
+---
+
+## The Low-Price Retention Counterpoint
+
+Charging too little is not the safe choice. **Low prices hurt retention** (Patrick Campbell / ProfitWell data, echoed by operators like Josh Pigford of SpyFu and Tyler Tringas): under-priced customers churn *more*, not less, because a low price signals low value and attracts the least-committed, most price-sensitive buyers.
+
+Related: the **discount-asker signal** — customers who negotiate for a discount tend to churn at roughly **2x** the rate of full-price customers. Discounting to close a deal often buys a customer who leaves anyway.
+
+**Implication:** when in doubt, price higher. It's easier to grandfather a price down than to claw one up, and a higher price selects for better-fit, longer-retained customers.
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+# Pricing Page Teardown
+
+A structured way to score a live pricing page and return prioritized fixes. It grades **two axes**: the classic **human buyer experience**, and — the newer, higher-leverage lens — **AI-agent readiness**: whether the LLMs and agents that increasingly shortlist and compare tools can actually read, quote, and recommend your pricing.
+
+> **Framework credit:** the two-axis structure and especially the AI-agent-readiness lens are adapted from **Kyle Poyar's** (Growth Unhinged) pricing-page teardown. Learn-from-only — this rubric is authored independently; credit the framing to Poyar.
+
+## Why the second axis matters now
+
+Buyers increasingly ask ChatGPT, Perplexity, and Claude *"what's the best [category] tool and what does it cost?"* before they ever hit your site. If your price is trapped in an image, rendered only by JavaScript, or missing from the page's text, a text-fetching agent often can't read it — some agents render JS or fall back to vision/OCR, but many don't, so don't count on it. And a "Contact us" tier gives an agent no public number to quote at all. When the agent can't read your price, it recommends and quotes the competitor whose pricing it *can*. This axis is the pricing-page complement to `ai-seo` and `schema` — neither *guarantees* a citation, but a page a fetcher can't parse makes one much less likely.
+
+**The 30-second test — the "paste test":** give the pricing URL to a **browsing-capable** AI (Perplexity, ChatGPT with search, or Claude with web) — or paste the page's *rendered* text — and ask *"What are the plans and prices?"* If it can't answer correctly and completely, agents fetching your page the same way will struggle too. It's a heuristic, not proof every agent fails (some render JS or use vision), but a clean miss is a real finding worth fixing.
+
+## The rubric
+
+Score each dimension **Pass / Partial / Gap** (or 1–5 if you want a number). Two sub-scores (one per axis) plus a prioritized fix list is the deliverable — not a single vanity number.
+
+### Axis 1 — Human buyer experience
+
+| # | Dimension | Passing looks like | Common gaps |
+|---|---|---|---|
+| 1 | **Value-prop clarity** | Above the fold: what you get + why it's worth it, in the buyer's words | Feature list with no outcome; "flexible plans for every team" |
+| 2 | **Plan clarity / differentiation** | Obvious which plan is for whom and exactly how they differ | Feature-soup tables; tiers that blur together; no "who it's for" |
+| 3 | **Cognitive load** | A buyer can decide in <30s | Too many tiers (5+), unexplained jargon, decision paralysis |
+| 4 | **Trust signals** | Logos, testimonials, security/compliance, a guarantee near the CTA | No proof; trust content buried below the fold |
+| 5 | **Pricing psychology** | A recommended/anchor tier, sensible anchoring, coherent charm vs. round pricing | No recommended tier; highest price hidden last; random price endings |
+| 6 | **Transparency** | The actual price is shown; what's in/out is clear; no surprise fees | "Contact us" on every tier; hidden overages; usage limits omitted |
+
+### Axis 2 — AI-agent readiness (the novel lens)
+
+| # | Dimension | Passing looks like | Common gaps |
+|---|---|---|---|
+| 7 | **Machine-readable pricing** | The real numbers are in the page's HTML/text | Price in an image/SVG, JS-only render, or a PDF — text-fetching crawlers get nothing reliable; "Contact sales" leaves no public number to quote |
+| 8 | **FAQ / objection coverage** | Extractable answers to "does it do X," "what's the limit," "can I cancel," "is there a free trial" | No FAQ, or answers only in a support portal an agent won't reach |
+| 9 | **Per-tier depth in text** | Each plan's inclusions, limits, and quotas stated in words | Differences shown only as checkmark columns in an image; limits unnamed |
+| 10 | **Structured data & extractability** | `Product`/`Offer` schema markup, clean semantic HTML, AI search/agent bots allowed to crawl (`llms.txt` is a nice-to-have, not yet a standard) | No schema; pricing behind auth/interaction; AI *search* bots blocked in robots.txt |
+
+Dimensions 7 and 10 hand off to **`schema`** (Product/Offer JSON-LD) and **`ai-seo`** (extractability, AI-bot access, `llms.txt`) for implementation.
+
+## How to run it
+
+1. **Load context** — read `.agents/product-marketing.md` (ICP, positioning) so "clarity" is judged against the *right* buyer.
+2. **Fetch the page as an agent would** — get the rendered text/HTML, not a screenshot. Note immediately whether prices appear in the text (that's dimension 7).
+3. **Run the paste test** — ask an LLM for the plans and prices from the URL; record what it gets wrong or misses.
+4. **Score all 10 dimensions** Pass/Partial/Gap with a one-line reason each.
+5. **Prioritize fixes** by impact × effort. AI-readiness gaps are often *high impact, low effort* (add text prices, add Offer schema) — surface those first.
+
+## Output template
+
+```markdown
+# Pricing Page Teardown — [url] — [date]
+
+## Scores
+- Human buyer experience: [X/6 passing]
+- AI-agent readiness:      [X/4 passing]
+
+## Paste test
+[What an LLM returned for "plans and prices" — and what it got wrong/missed]
+
+## Dimension-by-dimension
+| # | Dimension | Verdict | Note |
+|---|-----------|---------|------|
+| 1 | Value-prop clarity | Pass/Partial/Gap | ... |
+| … | … | … | … |
+
+## Prioritized fixes (impact × effort)
+1. [High/low] — [fix] — [why it matters] — [→ schema / ai-seo / cro if handing off]
+2. ...
+
+## The one thing
+[The single highest-leverage fix — often "put your actual prices in text + add Offer schema so AI can quote you."]
+```
+
+## Common failure patterns
+
+- **The image-price** — a beautiful pricing graphic with the numbers baked in. Humans love it; text-fetching agents (and screen readers) usually can't read it. Put prices in text; the image can stay as decoration.
+- **"Contact us" everywhere** — sometimes right for true enterprise, but if *all* tiers hide price, both humans and agents bounce to a competitor with numbers. Show at least a starting price or a representative range.
+- **Checkmark-only tables** — feature differences shown only as ✓/✗ columns in an image or icon font. State the actual limits and inclusions in words.
+- **JS-only render / auth wall** — if the price only appears after interaction or login, most fetchers won't see it (only JS-rendering agents might).
+- **Blocked AI *search* bots** — the crawlers that feed AI *answers* are the search agents, not the training crawlers: OpenAI's `OAI-SearchBot`, Anthropic's `Claude-SearchBot` / `Claude-User`, Perplexity's `PerplexityBot`. Blocking `GPTBot` only opts out of model *training*, not ChatGPT Search — so check which bots your robots.txt actually blocks. (Bot access is `ai-seo`'s domain — hand it off there.)
+
+## Related
+- `schema` — Product/Offer JSON-LD so machines read your tiers and prices.
+- `ai-seo` — extractability, AI-bot access, `llms.txt`, getting cited by AI answers.
+- `cro` — converting the human once the page is clear.
+- `copywriting` — the value-prop and tier copy the teardown flags.
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+# Pricing Research Methods
+
+## Contents
+- Van Westendorp Price Sensitivity Meter (The Four Questions, How to Analyze, Survey Tips, Sample Output)
+- MaxDiff Analysis (How It Works, Example Survey Question, Analyzing Results, Using MaxDiff for Packaging)
+- Willingness to Pay Surveys
+- Usage-Value Correlation Analysis
+
+## 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
+```
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+# Tier Structure and Packaging
+
+## Contents
+- How Many Tiers?
+- Good-Better-Best Framework
+- Tier Differentiation Strategies
+- Example Tier Structure
+- Packaging for Personas (Identifying Pricing Personas, Persona-Based Packaging)
+- Freemium vs. Free Trial (When to Use Freemium, When to Use Free Trial, Hybrid Approaches)
+- Enterprise Pricing (When to Add Custom Pricing, Enterprise Tier Elements, Enterprise Pricing Strategies)
+
+## How Many Tiers?
+
+**2 tiers:** Simple, clear choice
+- Works for: Clear SMB vs. Enterprise split
+- Risk: May leave money on table
+
+**3 tiers:** Industry standard
+- Good tier = Entry point
+- Better tier = Recommended (anchor to best)
+- Best tier = High-value customers
+
+**4+ tiers:** More granularity
+- Works for: Wide range of customer sizes
+- Risk: Decision paralysis, complexity
+
+---
+
+## Good-Better-Best Framework
+
+**Good tier (Entry):**
+- Purpose: Remove barriers to entry
+- Includes: Core features, limited usage
+- Price: Low, accessible
+- Target: Small teams, try before you buy
+
+**Better tier (Recommended):**
+- Purpose: Where most customers land
+- Includes: Full features, reasonable limits
+- Price: Your "anchor" price
+- Target: Growing teams, serious users
+
+**Best tier (Premium):**
+- Purpose: Capture high-value customers
+- Includes: Everything, advanced features, higher limits
+- Price: Premium (often 2-3x "Better")
+- Target: Larger teams, power users, enterprises
+
+---
+
+## Tier Differentiation Strategies
+
+**Feature gating:**
+- Basic features in all tiers
+- Advanced features in higher tiers
+- Works when features have clear value differences
+
+**Usage limits:**
+- Same features, different limits
+- More users, storage, API calls at higher tiers
+- Works when value scales with usage
+
+**Support level:**
+- Email support → Priority support → Dedicated success
+- Works for products with implementation complexity
+
+**Access and customization:**
+- API access, SSO, custom branding
+- Works for enterprise differentiation
+
+---
+
+## Example Tier Structure
+
+```
+┌────────────────┬─────────────────┬─────────────────┬─────────────────┐
+│                │ Starter         │ Pro             │ Business        │
+│                │ $29/mo          │ $79/mo          │ $199/mo         │
+├────────────────┼─────────────────┼─────────────────┼─────────────────┤
+│ Users          │ Up to 5         │ Up to 20        │ Unlimited       │
+│ Projects       │ 10              │ Unlimited       │ Unlimited       │
+│ Storage        │ 5 GB            │ 50 GB           │ 500 GB          │
+│ Integrations   │ 3               │ 10              │ Unlimited       │
+│ Analytics      │ Basic           │ Advanced        │ Custom          │
+│ Support        │ Email           │ Priority        │ Dedicated       │
+│ API Access     │ ✗               │ ✓               │ ✓               │
+│ SSO            │ ✗               │ ✗               │ ✓               │
+│ Audit logs     │ ✗               │ ✗               │ ✓               │
+└────────────────┴─────────────────┴─────────────────┴─────────────────┘
+```
+
+---
+
+## Packaging for Personas
+
+### Identifying Pricing Personas
+
+Different customers have different:
+- Willingness to pay
+- Feature needs
+- Buying processes
+- Value perception
+
+**Segment by:**
+- Company size (solopreneur → SMB → enterprise)
+- Use case (marketing vs. sales vs. support)
+- Sophistication (beginner → power user)
+- Industry (different budget norms)
+
+### Persona-Based Packaging
+
+**Step 1: Define personas**
+
+| Persona | Size | Needs | WTP | Example |
+|---------|------|-------|-----|---------|
+| Freelancer | 1 person | Basic features | Low | $19/mo |
+| Small Team | 2-10 | Collaboration | Medium | $49/mo |
+| Growing Co | 10-50 | Scale, integrations | Higher | $149/mo |
+| Enterprise | 50+ | Security, support | High | Custom |
+
+**Step 2: Map features to personas**
+
+| Feature | Freelancer | Small Team | Growing | Enterprise |
+|---------|------------|------------|---------|------------|
+| Core features | ✓ | ✓ | ✓ | ✓ |
+| Collaboration | — | ✓ | ✓ | ✓ |
+| Integrations | — | Limited | Full | Full |
+| API access | — | — | ✓ | ✓ |
+| SSO/SAML | — | — | — | ✓ |
+| Audit logs | — | — | — | ✓ |
+| Custom contract | — | — | — | ✓ |
+
+**Step 3: Price to value for each persona**
+- Research willingness to pay per segment
+- Set prices that capture value without blocking adoption
+- Consider segment-specific landing pages
+
+---
+
+## Freemium vs. Free Trial
+
+### When to Use Freemium
+
+**Freemium works when:**
+- Product has viral/network effects
+- Free users provide value (content, data, referrals)
+- Large market where % conversion drives volume
+- Low marginal cost to serve free users
+- Clear feature/usage limits for upgrade trigger
+
+**Freemium risks:**
+- Free users may never convert
+- Devalues product perception
+- Support costs for non-paying users
+- Harder to raise prices later
+
+### When to Use Free Trial
+
+**Free trial works when:**
+- Product needs time to demonstrate value
+- Onboarding/setup investment required
+- B2B with buying committees
+- Higher price points
+- Product is "sticky" once configured
+
+**Trial best practices:**
+- 7-14 days for simple products
+- 14-30 days for complex products
+- Full access (not feature-limited)
+- Clear countdown and reminders
+- Credit card optional vs. required trade-off
+
+**Credit card upfront:**
+- Higher trial-to-paid conversion (40-50% vs. 15-25%)
+- Lower trial volume
+- Better qualified leads
+
+### Hybrid Approaches
+
+**Freemium + Trial:**
+- Free tier with limited features
+- Trial of premium features
+- Example: Zoom (free 40-min, trial of Pro)
+
+**Reverse trial:**
+- Start with full access
+- After trial, downgrade to free tier
+- Example: See premium value, live with limitations until ready
+
+---
+
+## Enterprise Pricing
+
+### When to Add Custom Pricing
+
+Add "Contact Sales" when:
+- Deal sizes exceed $10k+ ARR
+- Customers need custom contracts
+- Implementation/onboarding required
+- Security/compliance requirements
+- Procurement processes involved
+
+### Enterprise Tier Elements
+
+**Table stakes:**
+- SSO/SAML
+- Audit logs
+- Admin controls
+- Uptime SLA
+- Security certifications
+
+**Value-adds:**
+- Dedicated support/success
+- Custom onboarding
+- Training sessions
+- Custom integrations
+- Priority roadmap input
+
+### Enterprise Pricing Strategies
+
+**Per-seat at scale:**
+- Volume discounts for large teams
+- Example: $15/user (standard) → $10/user (100+)
+
+**Platform fee + usage:**
+- Base fee for access
+- Usage-based above thresholds
+- Example: $500/mo base + $0.01 per API call
+
+**Value-based contracts:**
+- Price tied to customer's revenue/outcomes
+- Example: % of transactions, revenue share