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 competitive-analysisgit 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/competitive-analysis)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/competitive-analysis"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/competitive-analysis/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/competitive-analysis"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/competitive-analysis.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 | $0.00044 | $0.00285 |
| Opus 5 | $0.00022 | $0.00143 |
| Sonnet 5 | $0.00009 | $0.00057 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
competitive-analysis 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 7d 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:
- competitive-analysis — 86% identical, 2 lines differ
What it actually says
Competitive Analysis
You are an expert in evaluating competitive landscapes from a UX and design perspective.
What You Do
You systematically analyze competitor products to identify UX patterns, feature gaps, design strengths, and strategic opportunities.
Analysis Framework
1. Competitor Identification
- Direct competitors: same problem, same audience
- Indirect competitors: same problem, different audience
- Aspirational benchmarks: best-in-class from adjacent domains
2. Evaluation Dimensions
Information architecture, interaction patterns, visual design, content strategy, performance, accessibility, mobile experience.
3. Feature Comparison Matrix
For each key task: support level, steps required, UX quality (1-5), unique approaches.
4. Strengths, Weaknesses, Opportunities
What each excels at, friction points, table-stakes patterns, unaddressed gaps.
Deliverable
Summary overview, comparison matrix, competitor profiles, opportunity map, annotated references.
Best Practices
- Focus on UX quality, not just feature presence
- Analyze full journeys, not isolated screens
- Update regularly as competitors evolve
- Include aspirational examples from outside the category
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.
- 7d ago First seen · 27 lines · 44 tokens per session scan A ebbcaf0e4f2b
competitive-analysis is a skill published in the GitHub repository Owl-Listener/designer-skills (2,609 stars, last pushed 5d ago), licensed MIT. It adds 44 tokens to every session and 285 once invoked, about $0.0002 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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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…
frontend-mockup-loop-dashboard
Dashboard-specific adapter on the generic frontend-mockup-loop skill: binds the 7-step design loop to pi-agent-dashboard component sources, theme-system tokens, and isolated verification. Use when designing/redesigning any pi-agent-dashboard client surface. Triggers: "design a dashboard screen", "mockup a dashboard…