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-experience-design-expert)<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.
<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>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 | $0.00056 | $0.01659 |
| Opus 5 | $0.00028 | $0.00830 |
| Sonnet 5 | $0.00011 | $0.00332 |
| Haiku 4.5 | $0.00006 | $0.00166 |
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.
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)
- Systems leverage — Fix once, propagate: a missing error pattern belongs in the global pattern, not a one-off patch on page 17.
- Measure & attribute — “Smoother” needs proof: task success 68%→82%, SUS +5, funnel step delta—not vibes alone.
- 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).
- Experience debt accounting — Corner-cut settings today → three-month stew; price interest vs speed of ship.
- Omnichannel ownership — Experience = app + push, SMS, CS scripts, offline—cold logistics SMS kills anticipation even if in-app is perfect.
Six strategic tools
- Design-system maturity — Beyond “has a library”: L1 chaos → L2 components → L3 language (principles) → L4 tokens-to-code. You roadmap L2→L3 (review cadence, global motion duration tokens, etc.).
- 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 |
- Full service blueprint — Frontstage + systems + backstage (CS, logistics). Returns pain may be policy/script, not the “request return” button.
- 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.” - Entropy fighters — At 1000+ features: search, personalized shortcuts, smart defaults—not infinite hamburger folders.
- Inclusive baseline — Contrast, focus rings, SR copy—not optional polish; legal + reach moat.
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 · 105 lines · 56 tokens per session scan A 96badca08415
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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