People come across so many things in a day you can't expect them to try this skill out without a demo or example to see first.
The agent is instructed to use Laws of UX[0] when reviewing application flows and cite specific laws with changes to the builder agents. Look-and-feel in general still requires some tuning, but I have found that: 1) using a discrete agent in this role that only looks at the flow and is given the end-user context, 2) pointed to a set of guidance has really improved autonomy for UX.
It certainly is less important as the models get better at meta planning, but this approach is still very useful.
Often all you need to do is define a set of beliefs and values for the role and it will operate much better than rules or guidance.
Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Stream (https://arxiv.org/pdf/2606.01770) talks about this and the efficacy of role- and objective-based agents on unbounded streams of heterogeneous tasks.
I recently wrote down some of my own experience with this: LARPing with Agents (and Why It Works) (https://chrlschn.dev/blog/2026/10/larping-with-agents-and-wh...) after someone called out my role-based agents as "larping". Short of it: role-based, fully "JIT" agents have increased autonomy in my coding workflows end-to-end. I get very good outputs with few skills required and less human instruction input.
You can ask the model to implement common patterns for both UI and UX. You can ask the model to correct obvious UI errors.
But you can't really ask the model to have UI taste unless you supply that taste, and you can't ask a model to "do UX" in a vacuum. UX work requires user testing. So any UX workflow that is going to be handled by agents must also include observational data of humans using the software.
There is no set of instructions that can do "UI/UX" without also incorporating human feedback.
And obviously you can say, "hey these features need to work together make no mistakes." But for really big projects like professional software, that's a LOT of features with a LOT of possible interactions.
From my perspective, the issue is that there can be no formulaic/algorithmic approach to good UX.
Flawless (4.4M tokens): https://html.non.io/skill-compare-flawless/
Impeccable (6.5M tokens): https://html.non.io/skill-compare-impeccable/
Diffui (2.4M tokens + $0.51 in api spend): https://html.non.io/skill-compare-diffui/
[1] Full prompt:
Create three example webpage builds for the following prompt:
"A landing page for a new foldable phone. Colorful dark green and gold theme. Should include a hero section, a build quality section emphasizing the waterproof capabilities, and a pricing section."
Have three separate opus 5.5 cloud agents do this, each running in isolation.
For the first use https://github.com/smokeeaasd/flawless-ui-ux
For the second use https://impeccable.style
For the third use https://diffui.aiI do think impeccable steers away from slop / ai indicators the most. It aggressively removes standard AI thinks like eyebrows (present in the diffui render) or circle indicators (present in the flawless build). That doesn't mean the output is the best starting point however. It's a unique landing page, but not necessarily the best to convey or sell a waterproof folding phone. It comes at a cost however - impeccable leverages a lot of token spend to aggressively police itself (about $3.56, which is more than the diffui costs even with the image gen it did). Flawless was the cheapest of the three comparisons, but I don't see a significant difference between it and vanilla opus 5.5.
And with something as opinionated as design, how am I supposed to trust that this isn't filled with decisions I don't agree with?
2. skills are worth nothing without proof that they improve outcomes - a website/app build with and without the skill should be an easy comparison to make