247 frameworks, rules, and principles from 10 practitioners — ready to drop into any AI agent.
Meng To, Jenny Wen, Karri Saarinen, Caitlin Kalinowski, Katie Dill, and 5 more
28
frameworks
66
rules
78
heuristics
75
principles
Generic AI gives you a checklist. Gavel gives you named experts who disagree — so you can decide who to follow.
"Our app works fine but it feels generic and cheap next to competitors. Where do we even start?"
Here are some things to consider:
The right choice depends on your specific situation...
"Our app works fine but it feels generic and cheap next to competitors. Where do we even start?"
"Quality is a stack, and you cannot skip a layer. First utility, does it solve a real problem. Then usability, a chair you can sit in but that is extremely uncomfortable will not work out for you for very long. Craft and beauty come third, and they are not decoration: beauty signals care, and care is what earns trust."
Katie Dill
Utility, Usability, Craft"Generic is what you get when nobody on the team has an opinion. Craft starts on the hiring front, with people who care, and it shows up as the absence of little paper cuts rather than as a redesign. Every one of those small annoyances is a reason someone stops using the thing."
Karri Saarinen
Opinionated SoftwareWhere They Disagree
Dill runs quality as a system: a review cadence where the team walks its own product on a schedule and logs friction, so quality survives a growing org. Saarinen argues the cadence is downstream of taste, and that hiring people with opinions beats any process. Both reject the AB test as the arbiter, which is exactly where a generic assistant sends you.
Real items from this skill pack. Every item includes expert attribution and source material.
Block-Frame Conceptual Foundation Stay in ultra-low-fidelity block frames long enough to lock the conceptual model before any high-resolution visual work, so feedback stays on substance rather than surface. Steps: 1. Represent screens as big chunky blocks showing only location and hierarchy 2. Debate and refine the conceptual model while fidelity is too low for visual critique 3. Only after the foundation is solid move to wireframes then high-res comps 4. Leverage a robust design system so high-res comps can be produced in a day once locked Why it works: High-resolution artifacts immediately pull attention to colors, shapes and copy; low-fidelity forces the team to solve the hard thinking first and prevents premature commitment. Common mistakes: - Jumping straight to high-fidelity prototypes or GenAI comps - Letting PMs or execs see polished visuals before the concept is solid - Treating visual polish as the hard part instead of the conceptual heavy lifting
Bob Baxley
35 years of product design wisdom from Apple, Disney, Pinterest and beyond | Bob Baxley
single_practitioner consensusAlign all founders explicitly on the importance of quality and craft before hiring; multiple conflicting cultures cannot coexist Context: Early company formation when founders come from different prior cultures
Karri Saarinen
Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO)
A healthy researcher workload is two big projects plus one small side project; more than that guarantees mediocre work. Context: PM-researcher prioritization
Judd Antin
The UX Research reckoning is here | Judd Antin (Airbnb, Meta)
Ranked by how many frameworks each practitioner contributes to this pack
247 expert-sourced frameworks, rules, and principles. One .md file. Drop it in and your AI cites practitioners instead of guessing.
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