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 Dragoon0x/everything-design-taste --skill ai-product-designgit clone --depth 1 https://github.com/Dragoon0x/everything-design-tasteWrote 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/dragoon0x/everything-design-taste/ai-product-design)<a href="https://agentmods.dev/skills/dragoon0x/everything-design-taste/ai-product-design"><img src="https://agentmods.dev/badge/skills/dragoon0x/everything-design-taste/ai-product-design/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/dragoon0x/everything-design-taste/ai-product-design"><img src="https://agentmods.dev/badge/skills/dragoon0x/everything-design-taste/ai-product-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00018 | $0.00291 |
| Opus 5 | $0.00009 | $0.00146 |
| Sonnet 5 | $0.00004 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
ai-product-design 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.
What it actually says
AI Product Design
Core Principles
- Set expectations: Tell users what AI can and cannot do
- Show confidence: Indicate AI's certainty level when relevant
- Allow override: Users can always correct or override AI decisions
- Be transparent: Show how AI arrived at its output when possible
- Fail gracefully: AI mistakes should be easy to spot and fix
Patterns
| Pattern | Example |
|---|---|
| Suggestion | Email autocomplete, search suggestions |
| Classification | Spam detection, content categorization |
| Generation | Text, image, code generation |
| Prediction | Estimated delivery time, churn prediction |
| Automation | Auto-tagging, smart replies |
AI Suggestion UX
- Show suggestion inline or as a separate element
- Accept: one tap/click or keyboard shortcut
- Reject: easy dismiss, don't re-suggest the same thing
- Edit: modify the suggestion before accepting
- Feedback: way to indicate if suggestion was helpful
AI Transparency
- 'AI-generated' label on AI content
- Confidence scores when relevant (but don't overwhelm)
- Source attribution for AI summaries
- Audit trail for AI decisions in business contexts
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 · 37 lines · 18 tokens per session scan A 204bf285ee29
ai-product-design is a skill published in the GitHub repository Dragoon0x/everything-design-taste (11 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 291 once invoked, about $0.0001 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.
Other skills, from other repositories
ai-product-design
Design, specify, map, evaluate, or improve an AI assistant, LLM feature, copilot, chatbot, agent, recommendation, generation, or automation workflow. Define capability boundaries, user control, recovery, trust, evidence, uncertainty, permissions, and evaluation. Trigger on "design this AI feature", "build an AI…
ai-component-patterns
Use when designing or implementing any of the 6 core AI UI components. Each has specific patterns, pitfalls, and required sub-components that generic UI components don't address.
prompt-ux-design
Use when designing the user-facing prompt experience for any AI feature. Covers input design, suggestion patterns, history, feedback signals, and the interaction model between user intent and model execution.
accessibility
Audit and improve web accessibility following WCAG 2.2 guidelines. Use when asked to "improve accessibility", "a11y audit", "WCAG compliance", "screen reader support", "keyboard navigation", or "make accessible".
ss-reference
Compile screenshots, URLs, Figma exports, or an existing UI into a project-local StyleSeed output grammar with evidence, tokens, confidence, anti-patterns, and a validation screen. Use when the user supplies a design reference that StyleSeed does not already model.
auteur
Design and build complete web experiences from scratch — award-level product and marketing pages, cinematic scroll-directed sites where the page is directed like a film, and multi-screen products (app, dashboard, admin, settings, onboarding, docs site) built as one design system with every component, state and route…