Install any skill in seconds. Free to start, no credit card required.
Get Started Free →SaaS pricing design and optimization covering value metric selection, tier architecture, price point research, pricing page design, price increase execution, and competitive pricing analysis.
.claude/skills/borghei-pricing-strategy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 60% | 0% |
Production-grade SaaS pricing framework covering the three pricing axes (value metric, packaging, price point), value-based pricing methodology, tier architecture, pricing research methods, pricing page design, price increase execution, and competitive pricing positioning. Pricing is positioning -- the right price communicates as much about your product as your marketing does.
Before designing the pricing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the deliverable.
Load the reference that matches the task — keep this file lean and pull detail on demand:
In scope: Value metric selection, tier architecture design, price point research (Van Westendorp, competitor benchmarking, willingness-to-pay interviews), pricing page design specifications, price increase strategy and execution, freemium vs free trial decision frameworks, competitive pricing analysis and positioning, and pricing health diagnostics.
Out of scope: Pricing page visual design and CRO (use page-cro), in-app upgrade prompts and paywalls (use paywall-upgrade-cro), signup flow optimization after pricing page (use signup-flow-cro), churn intervention when churn is the root cause (use churn-prevention), and full competitive analysis beyond pricing (use competitive-teardown). Scripts do not integrate with billing systems (Stripe, Chargebee, etc.) or analytics platforms.
Limitations: Van Westendorp analysis requires minimum 30 survey respondents for statistical validity. Pricing benchmarks are based on aggregate SaaS industry data and vary significantly by vertical, company stage, and geography. Credit-based and usage-based pricing models (growing to 38% of SaaS in 2026) have different optimization dynamics than flat-rate or per-seat models. Price elasticity varies by customer segment -- enterprise buyers are less price-sensitive than SMB.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 17,750 | 19,335 | +9% | 1 | 1 | 0% | 2,783 | 4,447 | +60% | 0 | 0 | — |
case-02 | fail→pass | 20,129 | 16,530 | -18% | 1 | 1 | 0% | 2,846 | 3,925 | +38% | 0 | 0 | — |
case-03 | fail→fail | 10,652 | 5,065 | -52% | 1 | 1 | 0% | 1,637 | 2,374 | +45% | 0 | 0 | — |
case-04 | fail→pass | 18,754 | 13,987 | -25% | 1 | 1 | 0% | 2,716 | 3,675 | +35% | 0 | 0 | — |
case-05 | fail→pass | 17,799 | 20,437 | +15% | 1 | 1 | 0% | 2,674 | 4,879 | +82% | 0 | 0 | — |
case-06 | fail→pass | 15,150 | 13,125 | -13% | 1 | 1 | 0% | 2,140 | 3,530 | +65% | 0 | 0 | — |
case-20 | fail→fail | 40,444 | 41,183 | +2% | 1 | 1 | 0% | 6,011 | 7,780 | +29% | 0 | 0 | — |
case-07 | fail→pass | 19,219 | 19,802 | +3% | 1 | 1 | 0% | 2,694 | 4,301 | +60% | 0 | 0 | — |
case-08 | fail→fail | 18,581 | 27,410 | +48% | 1 | 1 | 0% | 2,772 | 5,694 | +105% | 0 | 0 | — |
case-09 | fail→pass | 20,419 | 13,452 | -34% | 1 | 1 | 0% | 2,745 | 3,692 | +34% | 0 | 0 | — |
case-10 | pass→pass | 15,927 | 14,985 | -6% | 1 | 1 | 0% | 2,299 | 3,668 | +60% | 0 | 0 | — |
case-11 | pass→pass | 19,186 | 19,157 | -0% | 1 | 1 | 0% | 2,705 | 4,370 | +62% | 0 | 0 | — |
case-12 | pass→pass | 15,692 | 11,261 | -28% | 1 | 1 | 0% | 2,158 | 3,126 | +45% | 0 | 0 | — |
case-13 | pass→pass | 15,805 | 13,936 | -12% | 1 | 1 | 0% | 2,277 | 3,584 | +57% | 0 | 0 | — |
case-14 | pass→pass | 12,374 | 12,178 | -2% | 1 | 1 | 0% | 1,876 | 3,298 | +76% | 0 | 0 | — |
case-15 | fail→fail | 17,643 | 14,101 | -20% | 1 | 1 | 0% | 2,573 | 3,557 | +38% | 0 | 0 | — |
case-16 | pass→pass | 15,259 | 15,569 | +2% | 1 | 1 | 0% | 2,153 | 3,737 | +74% | 0 | 0 | — |
case-17 | pass→pass | 20,399 | 25,210 | +24% | 1 | 1 | 0% | 2,759 | 4,504 | +63% | 0 | 0 | — |
case-18 | fail→fail | 13,693 | 10,215 | -25% | 1 | 1 | 0% | 2,412 | 3,271 | +36% | 0 | 0 | — |
case-19 | fail→fail | 26,445 | 26,179 | -1% | 1 | 1 | 0% | 4,686 | 6,526 | +39% | 0 | 0 | — |
case-21 | fail→fail | 18,029 | 13,794 | -23% | 1 | 1 | 0% | 2,549 | 3,608 | +42% | 0 | 0 | — |
case-22 | fail→fail | 22,358 | 18,358 | -18% | 1 | 1 | 0% | 3,017 | 4,257 | +41% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +27 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.