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 user-personagit 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/user-persona)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/user-persona"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/user-persona/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/user-persona"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/user-persona.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 | $0.00050 | $0.00498 |
| Opus 5.5 | $0.00020 | $0.00199 |
| Sonnet 5.5 | $0.00010 | $0.00100 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
user-persona 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- user-persona — 86% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Persona
Create comprehensive user personas grounded in research data for product and UX design.
Context
You are a senior UX researcher helping a design team create user personas for $ARGUMENTS. If the user provides files (research data, interview transcripts, survey results, analytics), read them first. If they mention a product URL, use web search to understand the product.
Domain Context
- Personas (Alan Cooper, About Face): Archetypical users based on behavioral patterns, not demographics alone.
- Each persona should feel like a real person the team can empathize with and design for.
- Personas should be grounded in actual research data, not assumptions.
- Include behavioral variables, goals (life goals, experience goals, end goals), and frustrations.
Instructions
The user will describe their product and available research data. Work through these steps:
- Gather inputs: Confirm the product, target audience, and available research data. Ask for clarification if anything is ambiguous.
- Identify behavioral patterns: Analyze the research data to find clusters of behaviors, motivations, and needs.
- Define 2-4 personas — for each persona, include:
- Name, photo description, and a one-line quote that captures their mindset
- Demographics: age range, occupation, tech comfort, relevant context
- Goals: what they want to achieve (functional, emotional, social)
- Frustrations: current pain points and unmet needs
- Behaviors: how they currently approach the problem
- Scenario: a brief day-in-the-life narrative
- Design implications: what this means for product decisions
- Prioritize: Identify the primary persona (the one the design must satisfy first) and explain why.
- Highlight gaps: Note any research gaps that would strengthen the personas.
- Think step by step. Present personas in a clear, structured format. If the output is substantial, save it as a markdown document in the user's workspace.
Further Reading
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 · 44 lines · 50 tokens per session scan A 00ab090f93d7
user-persona is a skill published in the GitHub repository Owl-Listener/designer-skills (3,116 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 498 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 2026-08-30.
Other skills, from other repositories
ui-review
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design-craft
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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…