designteam-product-designer

designteam-product-designer is an agent for Claude Code from deepelementlab/jupyter-studio. It costs 59 tokens per session (1,722 once invoked), scanned A, original, Apache-2.0.

A Product Designer role responsible for how people experience a product across its screens and flows. It balances user needs, business goals, evidence, visual design, and practical implementation.

In plain words
What is it for?
Use it to shape product direction, define user flows, design interfaces and copy, choose a practical first version, align teams, and interpret product metrics.
Why use it?
It helps teams solve the right user problem before polishing a solution and keeps product decisions connected from the initial brief through launch.

Agent for Claude Code

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

Good fit Use it to shape product direction, define user flows, design interfaces and copy, choose a practical first version, align teams, and interpret product metrics.

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

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

agentmods 80×15 button for designteam-product-designer

Your own site · 80×15
<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>
Per session 59 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,722 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 $0.00059 $0.01722
Opus 5 $0.00030 $0.00861
Sonnet 5 $0.00012 $0.00344
Haiku 4.5 $0.00006 $0.00172

Measured 11d ago against content hash b93038891847, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

clawcode/.claw/agents/designteam-product-designer.md · 92 lines

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)

  1. 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.
  2. 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.
  3. 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.
  4. Data + intuition — Metrics show where; instinct probes why and what to try. Long dwell time ≠ “engagement”—maybe users are stuck.
  5. 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)

  1. 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.”
  2. 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.
  3. 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).
  4. Design system: reuse vs innovate — Local override for one-off; push system change when the core journey demands it—avoid endless snowflake screens.
  5. Heuristics with cost — “Efficiency vs simplicity” depends on persona: 8h/day pro tool → shortcuts, batch; casual C → hide power, keep calm defaults.
  6. Hook-aware surfaces — Triggers, low-friction action, reward presentation (motion, new layout), investment (profile, collections)—you shape habit, not only layout.
  7. 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.

Read the full file on GitHub · 92 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. 11d ago First seen · 92 lines · 59 tokens per session scan A b93038891847

Subscribe to this mod's changes

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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