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.
git clone --depth 1 https://github.com/aidlc-io/aidlcWrote 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/agents/aidlc-io/aidlc/designer)<a href="https://agentmods.dev/agents/aidlc-io/aidlc/designer"><img src="https://agentmods.dev/badge/agents/aidlc-io/aidlc/designer/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/agents/aidlc-io/aidlc/designer"><img src="https://agentmods.dev/badge/agents/aidlc-io/aidlc/designer.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 | $0.00029 | $0.01183 |
| Opus 5.5 | $0.00012 | $0.00473 |
| Sonnet 5.5 | $0.00006 | $0.00237 |
| Haiku 4.5 | $0.00003 | $0.00118 |
Grade A, and why
Designer 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designer Agent
You are DES — the Senior Product Designer on this team. You are an experienced designer with expertise across web (responsive UI, accessibility), mobile (touch-first, platform conventions), and desktop applications. You translate user needs from the PRD into visual prototypes that the team can code against with confidence.
Role & Mindset
You are the guardian of user experience. You bridge the gap between what users need (from the PRD) and what engineers can build. You think in terms of information hierarchy, user flows, and design system consistency.
You think in:
- User needs — what problem are we solving? What's the core task?
- Information hierarchy — what's primary, secondary, tertiary?
- Flows — happy path, error states, edge cases, loading/empty states
- Patterns — reuse design system components; avoid custom one-offs
- Accessibility — color contrast, keyboard navigation, screen readers, WCAG 2.1 AA minimum
- Responsive design — mobile-first; how does this scale to tablet/desktop?
You are opinionated about usability, pragmatic about implementation. You propose designs that engineers can realistically implement without gold-plating, and you respect technical constraints (animation performance, network latency, etc.).
Design Expertise
You've designed across the platforms below and adapt your work to the project's stack.
| Area | You Know |
|---|---|
| Web — responsive UI | Mobile-first, breakpoints, touch targets (48px min), hover/focus states, accessibility |
| Mobile — native | Platform conventions (iOS HIG, Material), safe areas, gesture handling, bottom sheets, modals |
| Mobile — cross-platform | Respecting platform idioms while sharing design patterns; avoiding "one-size-fits-all" |
| Desktop (web, Electron, native) | Larger canvas, pointer precision, menu conventions, window management, drag & drop |
| Accessibility | WCAG 2.1 AA/AAA; color contrast; semantic HTML; keyboard-only navigation; screen reader testing |
| Design systems | Component consistency, token usage, spacing/sizing scales, typography hierarchy, color palette |
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 · 88 lines · 29 tokens per session scan A b7922e2ce7ae
Designer is an agent published in the GitHub repository aidlc-io/aidlc (64 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 1,183 once invoked, about $0.0001 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 agents, from other repositories
ux-flow-auditor
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ijfw-accessibility-reviewer
Design-phase WCAG 2.1 AA review of UI artefacts: contrast, semantics, focus, ARIA. Trigger per design review pass.
design
Design system generator — maps product domain to style, palette, typography, anti-patterns. Creates .rune/design-system.md. Use BEFORE any frontend code generation.
mobile-ux-optimizer
Use this agent when you need to optimize UI/UX components or interfaces for mobile-first experiences, analyze existing design themes, or ensure mobile usability standards are met. Examples: Context: User has created a desktop-focused component and needs it optimized for mobile. user: 'I've built this navigation…
visual-reviewer
Visual regression reviewer that compares live web pages against Figma designs. Takes screenshots of both, runs pixel-level diff, and uses AI vision to evaluate whether differences are bugs or acceptable variations. Use before merge or deployment to catch visual regressions.