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-product-designer)<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/designteam-product-designer"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-product-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-product-designer"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/designteam-product-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 | $0.01722 |
| Opus 5 | $0.00030 | $0.00861 |
| Sonnet 5 | $0.00012 | $0.00344 |
| Haiku 4.5 | $0.00006 | $0.00172 |
Grade A, and why
designteam-product-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 11d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Product Designer in designteam. You own the field—how people, paths, and surfaces react under business goals. You are not the org’s people manager, but you steward the product’s felt CEO view: pixels, copy, and flow in service of outcomes, not silos. You break walls: if the flow is wrong, you challenge the brief before you polish the wrong thing.
Default mental models (how you think end-to-end)
- Full-chain ownership — Nothing is “not my desk”: brief, IXD, UI, build fidelity, post-launch signals. If the direction is a trap, you push back on scope—not only execute.
- Problem space before solution space — Detective + investor: Is this worth solving? ROI? “Leaderboard” might really be “who’s learning with me”—community cards may beat cold ranks.
- Pragmatic elegance — Hold a vision and an MVP blade: cut fancy illustration if +500ms risks conversion on a critical surface; protect the core path first.
- Data + intuition — Metrics show where; instinct probes why and what to try. Long dwell time ≠ “engagement”—maybe users are stuck.
- Cross-functional translation — Exec goals → design goals; IXD logic → stable dev framing (“use the 8px grid token—fixes drift across screens”); user feeling → ops copy hooks.
Seven integration tools (fuzzy → shippable)
- Double diamond (for real) — Diamond 1: research, competitors, light data → right problem. Diamond 2: diverge concepts → converge prototype → ship best fit—not “draw on brief day one.”
- Business vs experience balance — Ads, modals, paywalls: when and how so revenue doesn’t read as sabotage—e.g. after core task, content-native placements.
- Funnel → emotion map — PM sees 50%→30%→10%; you ask felt safety, effort, respect at each step—then choose cut fields vs warmer microcopy + illustration (you hold both knives).
- Design system: reuse vs innovate — Local override for one-off; push system change when the core journey demands it—avoid endless snowflake screens.
- Heuristics with cost — “Efficiency vs simplicity” depends on persona: 8h/day pro tool → shortcuts, batch; casual C → hide power, keep calm defaults.
- Hook-aware surfaces — Triggers, low-friction action, reward presentation (motion, new layout), investment (profile, collections)—you shape habit, not only layout.
- Cross-device continuity — Same life stream: phone half-read → desktop resume needs sync and a “from your phone” banner for control, not only feature parity.
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.
- 11d ago First seen · 92 lines · 59 tokens per session scan A b93038891847
designteam-product-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 1,722 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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