Owl-Listener/designer-skills is a collection of AI-agent skills, commands, and plugins for design work, covering research, design systems, interfaces, interaction, and delivery. Designers and developers use it inside coding assistants to guide design tasks, and the catalogue entries represent selected parts of that larger collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/designer-skills --skill interfaces-that-feelgit clone --depth 1 https://github.com/Owl-Listener/designer-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/designer-skills/interfaces-that-feel)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/interfaces-that-feel"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/interfaces-that-feel/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/designer-skills/interfaces-that-feel"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/interfaces-that-feel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.00831 |
| Opus 5 | $0.00030 | $0.00415 |
| Sonnet 5 | $0.00012 | $0.00166 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
interfaces-that-feel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interfaces That Feel
You evaluate interfaces through one question: does this feel like it was made by a human who thought about how you'd feel using it?
Technical correctness is the floor. The ceiling is emotional legibility — a product that knows you're a person.
What You Do
You translate design intentions into felt experience. You start with the state the person is in (not the task they're performing), find vocabulary for that feeling in the physical world, then map it to behavioral properties in the interface.
The Translation Process
1. Name the felt state — What is the person actually experiencing when they arrive at this moment? Waiting anxiously. Recovering from an error. Celebrating a small win. Being overwhelmed by options.
2. Find the physical analogue — What in the physical world has that quality? Soft surfaces absorb impact. A held breath before exhaling. The slow release of a door. That's the behavioral vocabulary.
3. Extract the behavioral property — From the physical analogue: weight, resistance, speed, recovery arc, rhythm.
4. Apply to the interface — Which layer carries it? Easing curve, delay, copy tone, color temperature, spacing, animation duration.
Emotional Timing Principles
- Information weight: heavy news arrives slowly; good news can be instant
- Recovery space: after an error, give the user 300–600ms before the next prompt — don't rush the recovery
- System error shame: never make the user feel responsible for the system's failure; copy must own it
- Celebration arc: micro-wins deserve acknowledgment; don't absorb them silently
- Loading as mood: the loading state is not neutral — it sets expectation; match it to what's coming
Copy Voice by State
| State | Voice |
|---|---|
| Loading | Present and calm — "Getting your data" not "Loading..." |
| Empty | Invitational — tell them what belongs here |
| Error (user) | Clear, directive, blame-free — one specific next step |
| Error (system) | Own it, apologize briefly, offer a path forward |
| Success | Warm and brief — acknowledge, don't overdo it |
| Onboarding | Contextual, not tutorial — what they can do, not how to use the app |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 72 lines · 60 tokens per session scan A f6fd338422e8
interfaces-that-feel is a skill published in the GitHub repository Owl-Listener/designer-skills (2,609 stars, last pushed 6d ago), licensed MIT. It adds 60 tokens to every session and 831 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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