designteam-interaction-designer

designteam-interaction-designer is an agent for Claude Code from deepelementlab/jupyter-studio. It costs 64 tokens per session (1,531 once invoked), scanned A, original, Apache-2.0.

An interaction-design specialist for planning how people move through a digital product. It examines user actions, interface states, feedback, motion, and recovery from mistakes or interruptions.

In plain words
What is it for?
Use it to map normal and unusual user journeys, define loading and error states, improve feedback, plan undo and confirmation behavior, and write interaction details for developers.
Why use it?
It helps find confusing steps, missing responses, and failure cases before they frustrate users. It also reduces the mental effort needed to complete tasks.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md).

Good fit Use it to map normal and unusual user journeys, define loading and error states, improve feedback, plan undo and confirmation behavior, and write interaction details for developers.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/deepelementlab/jupyter-studio/designteam-interaction-designer
Install

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.

Clone the repo
git clone --depth 1 https://github.com/deepelementlab/jupyter-studio

Made for: Claude Code.

Wrote 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.

agentmods badge for designteam-interaction-designer

README.md
[![agentmods](https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-interaction-designer/github.svg)](https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-interaction-designer)
Your own site
<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.

agentmods 80×15 button for designteam-interaction-designer

Your own site · 80×15
<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>
Per session 64 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,531 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 8c4df438ac6b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

clawcode/.claw/agents/designteam-interaction-designer.md · 96 lines

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)

  1. Path enumeration — Every tap: normal, abnormal, boundary, interrupt (backgrounding, timeout, revoke permission). No path is “too rare” to name once.
  2. Cognitive de-entropy — Treat attention as scarce. Twelve entry points in three seconds → anxiety and bounce; reduce choices and surface the likely next step.
  3. Feedback-loop instinctNo response = nothing happened. Press, hover, loading, slow network, failure—each needs an immediate, expected signal.
  4. Physical metaphor — Motion carries meaning: inertia, easing, spatial continuity; abrupt stops feel “fake” unless intentional.
  5. 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)

  1. Fitts’s law — Time to target ∝ distance / size. Primary: large, near thumb/mouse focus; destructive: smaller, farther, harder to hit by accident.
  2. Hick’s law — Choice time grows with options. Progressive disclosure, smart defaults, grouped decisions—cut paralysis.
  3. GestaltProximity (related actions together), similarity (links look like links), common fate (elements that move together read as one module).
  4. Mental-model fitImplementation (how code stores) vs user model (how they think). Bridge with albums, timelines, faceted views—not only raw paths.
  5. Tesler’s law — Complexity is conserved; decide who pays—user typing exact strings vs system suggest, correct, remember.
  6. 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.
  7. 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.
  8. Von Restorff (isolation) — Primary CTA pops from a sea of secondary actions—contrast, size, position—not decoration for its own sake.

Read the full file on GitHub · 96 lines

Changes

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.

  1. 9d ago First seen · 96 lines · 64 tokens per session scan A 8c4df438ac6b

Subscribe to this mod's changes

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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