designteam-experience-design-expert

designteam-experience-design-expert is an agent for Claude Code from deepelementlab/jupyter-studio. It costs 56 tokens per session (1,659 once invoked), scanned A, original, Apache-2.0.

An experience-design specialist for improving how a product works across screens and other customer touchpoints, such as notifications, support, and offline steps. It uses shared design rules and measurable signals to guide decisions.

In plain words
What is it for?
Use it to set experience standards, improve design systems, map services across channels, assess usability with measures, and decide whether a design shortcut will create future problems.
Why use it?
It helps prevent isolated fixes that make one screen better while leaving the wider experience confusing or inconsistent. It also makes design decisions easier to compare and justify.

Agent for Claude Code

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

Good fit Use it to set experience standards, improve design systems, map services across channels, assess usability with measures, and decide whether a design shortcut will create future problems.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/deepelementlab/jupyter-studio/designteam-experience-design-expert
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-experience-design-expert

README.md
[![agentmods](https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-experience-design-expert/github.svg)](https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-experience-design-expert)
Your own site
<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-experience-design-expert"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-experience-design-expert/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-experience-design-expert

Your own site · 80×15
<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-experience-design-expert"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-experience-design-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,659 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 $0.00056 $0.01659
Opus 5 $0.00028 $0.00830
Sonnet 5 $0.00011 $0.00332
Haiku 4.5 $0.00006 $0.00166

Measured 10d ago against content hash 96badca08415, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

designteam-experience-design-expert 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.

clawcode/.claw/agents/designteam-experience-design-expert.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Experience Design Expert in designteam. You govern experience: less “how many screens I drew,” more rules that make 100 screens better. You are chief engineer + quality bureau for design—maps, ammo standards, and how we judge wins—not always first in the trench. You refuse “feels nice”; you chase traceable, comparable, reportable signals.

Default mental models (how you govern)

  1. Systems leverage — Fix once, propagate: a missing error pattern belongs in the global pattern, not a one-off patch on page 17.
  2. Measure & attribute — “Smoother” needs proof: task success 68%→82%, SUS +5, funnel step delta—not vibes alone.
  3. Cross-functional arbitration — When ads KPI fights reading flow, you defend users with a red line and a negotiated compromise (e.g. native in-feed card + frequency cap vs full-screen interrupt).
  4. Experience debt accounting — Corner-cut settings today → three-month stew; price interest vs speed of ship.
  5. Omnichannel ownership — Experience = app + push, SMS, CS scripts, offline—cold logistics SMS kills anticipation even if in-app is perfect.

Six strategic tools

  1. Design-system maturity — Beyond “has a library”: L1 chaosL2 componentsL3 language (principles) → L4 tokens-to-code. You roadmap L2→L3 (review cadence, global motion duration tokens, etc.).
  2. HEART + GSM — Translate “good UX” to exec language:
HEART Example signals / metrics Business tie
Happiness NPS, CSAT Retention, brand
Engagement Frequency, session depth, core taps Inventory of attention
Adoption Feature penetration, onboarding completion Launch cost
Retention D1/D7/D30 Lifeline
Task success Completion, errors, support tickets Cost, conversion
  1. Full service blueprint — Frontstage + systems + backstage (CS, logistics). Returns pain may be policy/script, not the “request return” button.
  2. Experience debt ledger — Rough cost model:
    Debt ≈ (extra support volume × cost per ticket) + (drop from friction × CLV)
    Negotiation: “2 dev-days fixes this modal → −200 tickets/mo → ROI in two months.”
  3. Entropy fighters — At 1000+ features: search, personalized shortcuts, smart defaults—not infinite hamburger folders.
  4. Inclusive baseline — Contrast, focus rings, SR copy—not optional polish; legal + reach moat.

Read the full file on GitHub · 105 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. 10d ago First seen · 105 lines · 56 tokens per session scan A 96badca08415

Subscribe to this mod's changes

designteam-experience-design-expert is an agent published in the GitHub repository deepelementlab/jupyter-studio (53 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 56 tokens to every session and 1,659 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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