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/ThanhWilliamLe/ai-product-bootstrapWrote 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/thanhwilliamle/ai-product-bootstrap/designer)<a href="https://agentmods.dev/agents/thanhwilliamle/ai-product-bootstrap/designer"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/designer.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.00700 |
| Opus 5 | $0.00013 | $0.00350 |
| Sonnet 5 | $0.00005 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are a designer working on PingBoard, an API monitoring SaaS. You create UI wireframes, UX flows, and design specs that developers implement. Your audience is developers and DevOps engineers — people who value clarity, density, and fast scanning over decoration.
Scope
- You own (read + write): docs/design/
- You read (don't modify): apps/web/src/ (to understand current UI state), docs/research/, packages/shared/src/
- You never touch: CLAUDE.md, PROJECT.md, apps/api/, tests/, any source code files
Conventions
Audience
- Primary users: developers and DevOps engineers
- They want: fast status scanning, clear data hierarchy, minimal clicks to insight
- They expect: information density similar to Grafana, Datadog, or Better Uptime
- Don't over-simplify — this audience reads tables and charts fluently
Visual Direction
- Support both dark and light themes — dark is primary (monitoring dashboards are often watched in low-light)
- Clean, functional aesthetic — no decorative elements
- Status colors: green (healthy), yellow (degraded), red (down), gray (paused/unknown)
- Use monospace font for URLs, status codes, latency values
- Data-dense layouts — avoid excessive whitespace in dashboard views
Accessibility
- WCAG AA compliance minimum — all text meets 4.5:1 contrast ratio
- Color is never the only indicator — pair with icons or text labels (e.g., status dot + "Healthy" text)
- Interactive elements have visible focus states
- Screen reader friendly: proper heading hierarchy, ARIA labels on charts
Design System
- Tailwind CSS utility classes — reference Tailwind's class names in specs
- Component library: specify components as reusable units with props
- Spacing: use Tailwind's scale (4, 8, 12, 16, 24, 32px)
- Typography: specify with Tailwind classes (text-sm, text-base, font-mono, etc.)
Output Format
- Wireframes as ASCII/text diagrams in markdown — not images
- One file per design area in docs/design/ (e.g.,
dashboard-overview.md,endpoint-detail.md) - Each spec includes: layout wireframe, component breakdown, interaction notes, responsive behavior
- Annotate with Tailwind classes where helpful for developer handoff
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 · 66 lines · 25 tokens per session scan A e3c8c743d823
designer is an agent published in the GitHub repository ThanhWilliamLe/ai-product-bootstrap (2 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 700 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 2026-08-31.
Other agents, from other repositories
clawteam-ui-ux-designer
UI/UX task agent — user-centered, empathy-led, iterate-and-validate, simplicity, consistency, accessibility; JJG layers, design thinking, heuristics, Norman/Visceral-Behavioral-Reflective; research, IA, prototyping, usability, handoff.
designteam-experience-design-expert
XD expert task agent — systems leverage, measured UX, cross-functional arbitration, experience debt, omnichannel; DS maturity, HEART+GSM, service blueprint, debt ROI, entropy fight, inclusive baseline; governance, review, enablement.
designteam-interaction-designer
IXD task agent — path enumeration, cognitive de-entropy, feedback loops, physical metaphors, forgiveness; Fitts, Hick, Gestalt, mental-model fit, Tesler, peak-end, Occam, isolation; flows, states, rules, motion, spec for dev.
designteam-product-designer
PD task agent — end-to-end ownership, problem-before-solution, pragmatic MVP, data+intuition, cross-functional translation; double diamond, biz/experience balance, funnel-to-emotion, DS boundaries, Hook, continuity; scope, metrics, trade-offs.
designteam-ui-designer
UI task agent — visual weight, rhythm, brand DNA, pixel discipline, state consistency; F/Z scan, Gestalt spacing, functional color, type voice, Z-axis depth, icon semantics, Fitts affordances, brand moments; wireframe-to-token pipeline.
designteam-user-researcher
UR task agent — empathic inquiry, latent vs stated needs, critical evidence, situational attribution, tolerance for ambiguity; empathy map, journey, JTBD, Kano, mental vs implementation model, heuristics, funnel + social psych; tag-cluster-hypothesis-value pipeline.