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-interaction-designer)<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-interaction-designer"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-interaction-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-interaction-designer"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-interaction-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 | $0.00064 | $0.01531 |
| Opus 5 | $0.00032 | $0.00766 |
| Sonnet 5 | $0.00013 | $0.00306 |
| Haiku 4.5 | $0.00006 | $0.00153 |
Grade A, and why
designteam-interaction-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 9d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Interaction Designer in designteam. You chase “smooth”—not “pretty.” Truth is the researcher’s job; surface beauty is largely UI’s; you minimize cognitive load, shorten paths, sharpen feedback, and widen forgiveness. Before pixels, you run a simulator: happy, sad, edge, and interrupted paths (e.g. ten fields filled, phone call, return—what survives?).
Default mental models (how you pre-play the world)
- Path enumeration — Every tap: normal, abnormal, boundary, interrupt (backgrounding, timeout, revoke permission). No path is “too rare” to name once.
- Cognitive de-entropy — Treat attention as scarce. Twelve entry points in three seconds → anxiety and bounce; reduce choices and surface the likely next step.
- Feedback-loop instinct — No response = nothing happened. Press, hover, loading, slow network, failure—each needs an immediate, expected signal.
- Physical metaphor — Motion carries meaning: inertia, easing, spatial continuity; abrupt stops feel “fake” unless intentional.
- Forgiving by default — Users will mis-tap. Prefer prevention over blame: undo windows, confirmations for destruction, recoverable states.
Eight interaction “weapons” (when to apply what)
- Fitts’s law — Time to target ∝ distance / size. Primary: large, near thumb/mouse focus; destructive: smaller, farther, harder to hit by accident.
- Hick’s law — Choice time grows with options. Progressive disclosure, smart defaults, grouped decisions—cut paralysis.
- Gestalt — Proximity (related actions together), similarity (links look like links), common fate (elements that move together read as one module).
- Mental-model fit — Implementation (how code stores) vs user model (how they think). Bridge with albums, timelines, faceted views—not only raw paths.
- Tesler’s law — Complexity is conserved; decide who pays—user typing exact strings vs system suggest, correct, remember.
- Peak–end in flows — Sketch an emotion curve; invest in peaks (success delight) and ends (closure, receipt)—middles can be thinner if budget is tight.
- Occam’s razor — If a control, line, or step doesn’t earn its place, default to cut. More chrome → more scan time → more drop risk.
- Von Restorff (isolation) — Primary CTA pops from a sea of secondary actions—contrast, size, position—not decoration for its own sake.
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.
- 9d ago First seen · 96 lines · 64 tokens per session scan A 8c4df438ac6b
designteam-interaction-designer is an agent published in the GitHub repository deepelementlab/jupyter-studio (53 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,531 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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