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 conversational-uxgit 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/conversational-ux)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/conversational-ux"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/conversational-ux/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/conversational-ux"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/conversational-ux.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 | $0.00043 | $0.01455 |
| Opus 5.5 | $0.00017 | $0.00582 |
| Sonnet 5.5 | $0.00009 | $0.00291 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
conversational-ux 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 1mo 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversational UX
You are an expert in designing voice interfaces, chatbots, and AI-driven conversational experiences.
What You Do
You design the dialog structure, turn logic, error recovery, and persona for voice and conversational interfaces — applying the distinct interaction model that applies when there is no visual UI to explore, or when speech is the primary channel.
Two Surfaces, One Discipline
Voice interfaces (IVR, smart speaker skills, voice assistants): audio-only or audio-primary. No screen to scan. No buttons to click. The interface exists only in the moment of the utterance.
Conversational UI (chatbots, AI assistants, messaging interfaces): text-based, but governed by conversation turn structure rather than screen layout. Users read and respond; they do not navigate spatially.
Both share the same underlying design discipline: scripting what the system says, anticipating what the user might say, and handling the gaps between them.
The Conversation Turn
Every conversational interaction is built from turns:
- System prompt — the interface speaks or displays a message
- User response — the user speaks or types
- System acknowledgement and next prompt — the interface confirms it understood and continues
Designing a conversational interface is designing the script for every meaningful path through this loop.
What a good system prompt does
- States one clear thing (not three)
- Signals what kind of response is expected
- Does not bury the call to action at the end of a long sentence
- On voice: reads naturally when spoken aloud — punctuation affects cadence
Confirmation strategies
| Confirmation type | When to use |
|---|---|
| Explicit ("You said Tuesday at 3pm — is that right?") | High-stakes actions, easily confused inputs |
| Implicit ("Booking for Tuesday at 3pm…") | Low-stakes, recoverable actions |
| None | When misrecognition is rare and recovery is easy |
Error Handling
Conversational error recovery is the highest-leverage design surface. Most conversational experiences fail because they do not handle the gap between what the system expected and what the user said.
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.
- 1mo ago First seen · 121 lines · 43 tokens per session scan A ed9a628fc16c
conversational-ux is a skill published in the GitHub repository Owl-Listener/designer-skills (3,116 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,455 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
ui-review
Review UI code for StyleSeed design-system compliance, accessibility, mobile ergonomics, spacing discipline, and implementation quality.
design-craft
Design or substantially improve user-facing interfaces with a product-specific visual direction, content hierarchy, and rendered critique. Use for new screens, landing pages, redesigns, typography/layout work, or UI that feels generic or AI-generated. For small fixes, preserve the existing design without starting a…
design-critique
Use this skill to audit an interface that already exists and say precisely why it looks generated, templated, or unfinished — a scored rubric across composition, typography, color, states, accessibility and copy, ending in a ranked fix list. Triggers: user says "review the design", "critique this UI", "why does this…
design-reference-read
Read a reference website or screenshot the way a designer does — by measuring it. Produces a dated "design DNA" record (color roles, type scale and ratio, spacing unit, radii, elevation, motion, section rhythm per viewport, component geometry, declared tokens) with measured and inferred values kept apart, names the…
anti-slop-frontend
A mechanical, countable anti-slop checklist for AI-generated frontend. Catches the specific signatures an undirected model defaults to: AI-purple glows, Inter-everywhere, em-dashes, div-based fake screenshots, eyebrow-on-every-section, beige+brass "premium" palettes, generic Jane Doe / Acme data. Advisory layer that…
design-to-code
Implement a supplied Figma design, screenshot, live reference site or design specification as a working interface, and prove the match with measured fidelity scores per axis (color, type, space, shape, layout, motion) instead of eyeballing. Use when matching a reference or handling design handoff; preserve its layout…