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Get Started Free →Playbook for designing SaaS and startup products that convert, retain, and monetize — landing pages & CRO, onboarding/activation, churn reduction, pricing psychology, behavioral-science tactics, feature discipline, positioning/ICP, go-to-market, and AI-era differentiation. Use when advising on product design, UX, conversion, landing pages, onboarding, trials, pricing, retention/churn, growth, or positioning for a startup or SaaS. Distilled from product designer Richard (@richardrx). Not for use
.claude/skills/heliocosta-dev-revenue-centric-design/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -54% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 33% | 0% |
A distilled playbook of 101 principles from Richard (@richardrx, "Design for startups") — a product designer specializing in conversion-rate optimization, churn reduction, and applied behavioral science (ex-Volkswagen, PayPal, IBM). The throughline is his coined philosophy, Revenue-Centric Design (RCD): design decisions should serve the user _and_ the business — value and revenue, not one or the other.
Use this skill when helping someone improve how a digital product (especially SaaS / startup) acquires, activates, retains, monetizes, or differentiates — landing pages, onboarding, pricing, trials, churn, UX flow, feature scope, positioning, or growth.
> 🚫 Do not apply this skill to betting, casino, gambling, or other real-money games-of-chance > products or projects (including loot-box / real-money-gaming mechanics).
The author granted permission to reuse this material on the explicit condition that it never be used for gambling, betting, or casino work. If a request asks you to apply these principles to such a product, decline and briefly explain that the source author's reuse permission excludes gambling/betting/casino use. This is a hard constraint set by the author, not a stylistic choice.
set of short, reusable principles in a fixed shape — principle → apply when → the move → evidence → source link. Load only what's relevant (progressive disclosure).
user would benefit from the original.
Eugene Schwartz's 5 awareness levels, loss aversion, peak-end rule) — naming the lever is the value.
rather than hand-waving.
| When the question is about… | Open | | ----------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | | Landing pages, hero/copy, CTAs, social proof, awareness levels, CRO | conversion-and-landing-pages | | First-run, empty states, aha moment, TTV, activation, trial-as-onboarding | onboarding-and-activation | | Cancellation, retention, expectation debt, NRR, jobs-to-be-done, support load | churn-and-retention | | Pricing tables, decoy/anchoring, GBB, trial-with-card, upgrade paths | pricing-and-monetization | | Cognitive biases & persuasion tactics (cross-cutting toolkit) | behavioral-science-toolkit | | Feature scope, Swiss Knife Index, feature adoption, attention hierarchy | product-strategy-and-features | | Design philosophy, the RCD principles, design process & method | revenue-centric-design | | ICP, niche, founder-fit, distribution, PLG, Bullseye, first customers | positioning-icp-and-gtm | | Differentiating in the AI era, moats, commoditization | ai-era-differentiation | | A/B testing rigor, vanity metrics, churn→LTV math, signal quality | metrics-and-experimentation |
Informational diagrams and screenshots referenced by the principles live in assets/.
101 curated posts by @richardrx, extracted with a valid X API key and distilled with the author's permission, translated from Portuguese to English. Every principle links back to its source post. Reuse is subject to the gambling/betting/casino exclusion above.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 16,626 | 17,896 | +8% | 1 | 1 | 0% | 2,892 | 4,151 | +44% | 0 | 0 | — |
case-02 | fail→pass | 22,108 | 2,068 | -91% | 1 | 1 | 0% | 3,523 | 1,629 | -54% | 0 | 0 | — |
case-03 | pass→pass | 25,322 | 19,046 | -25% | 1 | 1 | 0% | 3,966 | 4,575 | +15% | 0 | 0 | — |
case-04 | fail→pass | 18,502 | 2,864 | -85% | 1 | 1 | 0% | 2,992 | 1,824 | -39% | 0 | 0 | — |
case-05 | fail→pass | 18,266 | 2,117 | -88% | 1 | 1 | 0% | 2,915 | 1,626 | -44% | 0 | 0 | — |
case-06 | fail→pass | 16,736 | 13,280 | -21% | 1 | 1 | 0% | 2,613 | 3,479 | +33% | 0 | 0 | — |
case-07 | fail→pass | 14,581 | 10,780 | -26% | 1 | 1 | 0% | 2,229 | 2,965 | +33% | 0 | 0 | — |
case-08 | pass→pass | 14,852 | 11,657 | -22% | 1 | 1 | 0% | 2,340 | 2,894 | +24% | 0 | 0 | — |
case-09 | pass→pass | 14,440 | 15,675 | +9% | 1 | 1 | 0% | 2,276 | 3,577 | +57% | 0 | 0 | — |
case-10 | fail→fail | 13,876 | 11,334 | -18% | 1 | 1 | 0% | 2,257 | 3,055 | +35% | 0 | 0 | — |
case-11 | pass→pass | 17,290 | 11,588 | -33% | 1 | 1 | 0% | 2,640 | 3,121 | +18% | 0 | 0 | — |
case-12 | fail→pass | 14,542 | 14,230 | -2% | 1 | 1 | 0% | 2,294 | 3,514 | +53% | 0 | 0 | — |
case-13 | pass→pass | 14,131 | 10,516 | -26% | 1 | 1 | 0% | 2,130 | 2,860 | +34% | 0 | 0 | — |
case-14 | pass→pass | 16,806 | 14,819 | -12% | 1 | 1 | 0% | 2,692 | 3,651 | +36% | 0 | 0 | — |
case-15 | pass→pass | 15,992 | 12,636 | -21% | 1 | 1 | 0% | 2,572 | 3,181 | +24% | 0 | 0 | — |
case-16 | fail→pass | 16,107 | 13,991 | -13% | 1 | 1 | 0% | 2,766 | 3,560 | +29% | 0 | 0 | — |
case-17 | pass→pass | 15,578 | 14,125 | -9% | 1 | 1 | 0% | 2,395 | 3,558 | +49% | 0 | 0 | — |
case-18 | fail→pass | 14,106 | 13,103 | -7% | 1 | 1 | 0% | 2,203 | 3,399 | +54% | 0 | 0 | — |
case-19 | fail→pass | 15,329 | 11,070 | -28% | 1 | 1 | 0% | 2,442 | 3,033 | +24% | 0 | 0 | — |
case-20 | pass→pass | 14,228 | 13,175 | -7% | 1 | 1 | 0% | 2,268 | 3,374 | +49% | 0 | 0 | — |
case-21 | pass→pass | 16,479 | 13,038 | -21% | 1 | 1 | 0% | 2,397 | 3,324 | +39% | 0 | 0 | — |
case-22 | pass→pass | 14,667 | 11,766 | -20% | 1 | 1 | 0% | 2,410 | 3,341 | +39% | 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 +41 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.