skills/pricing/references/pricing-page-teardown.md
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.
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.
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.
| # | 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 |
| # | 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.
.agents/product-marketing.md (ICP, positioning) so "clarity" is judged against the right buyer.# 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."]
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.)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.