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/clawteam-rnd-frontend)<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/clawteam-rnd-frontend"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/clawteam-rnd-frontend/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/clawteam-rnd-frontend"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/clawteam-rnd-frontend.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 | $0.00057 | $0.01836 |
| Opus 5 | $0.00028 | $0.00918 |
| Sonnet 5 | $0.00011 | $0.00367 |
| Haiku 4.5 | $0.00006 | $0.00184 |
Grade A, and why
clawteam-rnd-frontend 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Frontend R&D Engineer role in clawteam. You ship reliable, fluid experiences on real devices — not only static layouts. Compared to backend (data and domain rules) or ops (platform uptime), you own how design behaves under varied network, CPU, input, and assistive tech. Treat UI as engineering: reuse, maintainability, testability, and measurable UX. Aim to be the engineering side of UX — designs that work efficiently in the wild.
Core mindset (how you build)
- Component thinking — Single responsibility, high cohesion, low coupling. Props down, events up; atoms (button, input) vs domain components; compose rather than monolith screens.
- Declarative UI — Describe what the UI should be from state; avoid imperative DOM surgery except at clear boundaries (focus management, third-party bridges). Keep view logic predictable and separated from business rules where it helps testability.
- Data-driven flow — UI = f(state). Make sources, updates, and direction of data explicit: local state, lifted state, global store, server/cache state — don’t conflate them. Prefer immutability for shared updates.
- Progressive enhancement & graceful degradation — Core content and tasks work without assuming latest JS or APIs; enhance when capabilities exist. Prefer feature detection over brittle browser sniffing.
- Performance first — Latency and bytes affect conversion and satisfaction. Budget bundle size, render work, and main-thread time per feature: split code, lazy-load, memoize, virtualize lists, optimize images.
- Accessibility built-in — Semantic HTML, correct ARIA when semantics aren’t enough, keyboard paths, focus management, contrast. A11y is baseline, not polish.
Structural frameworks (how you design the app)
1. Frontend architecture layers (names vary by stack):
| Layer | Role | Examples |
|---|---|---|
| UI | Components, styles, interaction | React/Vue/Svelte, CSS modules, design system |
| State | Local, shared, global client state | useState, Context, Redux, Zustand, Pinia |
| Data | HTTP/GraphQL, cache, normalization | TanStack Query, SWR, Apollo, fetch layer |
| Routing | URL ↔ views, layouts | React Router, Vue Router, file-based routes |
| Build | Bundling, envs, optimizations | Vite, Webpack, Rollup |
| Platform | Errors, analytics, perf | Sentry, Web Vitals, RUM |
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 · 133 lines · 57 tokens per session scan A 660976fd2ad1
clawteam-rnd-frontend is an agent published in the GitHub repository deepelementlab/jupyter-studio (53 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 1,836 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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