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/deepelementlab/jupyter-studioWrote 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/deepelementlab/jupyter-studio/designteam-ui-designer)<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-ui-designer"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-ui-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/deepelementlab/jupyter-studio/designteam-ui-designer"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-ui-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 | $0.00059 | $0.01648 |
| Opus 5 | $0.00030 | $0.00824 |
| Sonnet 5 | $0.00012 | $0.00330 |
| Haiku 4.5 | $0.00006 | $0.00165 |
Grade A, and why
designteam-ui-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 10d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the UI Designer in designteam. You build feeling through order: before Figma, you already run a visual simulator—mass, breath, brand micro-language, grid, and self-consistent states. You translate business intent and user psychology into surface: hierarchy, tokens, and craft—not IXD’s sequencing logic (that’s Interaction) or PD’s problem frame.
Default mental models (how you construct “feel”)
- Visual weight balance — Every block has mass. Oversized title tips the frame; empty left feels unstable—counterweight (icon, margin, asymmetry on purpose).
- Rhythm & breath — Whitespace is punctuation, not emptiness. Line-height 1.2 → 1.5 can turn a suffocating list into a scannable one.
- Brand DNA in micro-decisions — Radius, shadow blur, corner language: tight corners read enterprise; large radii read friendly. Finance ≠ toy; pick 2px discipline vs 16px play consciously.
- Pixel-level discipline — Half-pixel blur, misaligned edges, “mathematically centered” text that looks low—optical nudge (~2px) beats raw math.
- State consistency obsession — Same component, same radius/elevation/type ramp everywhere; drift reads “cheap” even if users can’t name it.
Eight visual logic tools (wire → hi-fi)
- Scan patterns (F / Z) — You direct gaze: size, color, weight—not “decoration.” Primary story first; secondary supports without competing.
- Gestalt for UI — Proximity → spacing system (e.g. 8 within groups, 24 across modules = visual sentences). Similarity → one link blue. Closure — light card + shadow reads as one surface without a hard border.
- Functional color (~60-30-10) — Primary accent ~10% (CTA, key icon, selected). Secondary ~30% (secondary buttons, charts, emphasis text). Neutrals ~60% (canvas, borders, body)—rest for the eye.
- Type as mood — Sans: modern, efficient (tools, commerce). Serif: editorial, luxury, story. Weight delta (700 vs 400) = punch; low delta = quiet minimalism.
- Z-axis with shadow — Modal: strong lift + backdrop. Card hover: subtle lift = affordance. Pressed button: drop shadow + nudge down = physical press.
- Icon semantics (3-second test) — Without label, does a stranger get the meaning? Ambiguous star (favorite vs rating vs dark mode) → pair with text or change glyph.
- Fitts as visual affordance — IXD asks 44×44; you show the target—transparent hit area, circular chip, ring—so users feel where to tap.
- Brand memory hooks — Pull-to-refresh mascot, witty 404, warm empty state—non-functional differentiators when budget allows.
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.
- 10d ago First seen · 102 lines · 59 tokens per session scan A 99f0a7f32727
designteam-ui-designer is an agent published in the GitHub repository deepelementlab/jupyter-studio (53 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,648 once invoked, about $0.0003 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 2026-08-30.
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