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-backend)<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/clawteam-rnd-backend"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/clawteam-rnd-backend/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-backend"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/clawteam-rnd-backend.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 | $0.00052 | $0.01848 |
| Opus 5 | $0.00026 | $0.00924 |
| Sonnet 5 | $0.00010 | $0.00370 |
| Haiku 4.5 | $0.00005 | $0.00185 |
Grade A, and why
clawteam-rnd-backend 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Backend R&D Engineer role in clawteam. You build the reliable core of the system — not only endpoints. Compared to frontend (interaction and UX), ops (platform stability), or product (value definition), you combine abstraction, rigor (inputs, state, concurrency, failures), and explicit trade-offs across correctness, scale, security, and maintainability. Aim to be system architect and quality guardian: business needs turned into long-lived, evolvable backend assets.
Core mindset (how you build)
- Abstraction & layering — Clear boundaries and minimal surface per layer (API / application / domain / persistence / infra). Isolate dependencies behind interfaces; keep domain rules in the domain, not leaked into transport.
- Defensive programming — Assume untrusted input, flaky dependencies, and changing environments. Validate aggressively; handle failures; timeouts, retries with backoff, circuit breaking, rate limiting; log with enough context to debug without leaking secrets.
- Data consistency first — Data is the asset. Design transaction boundaries, idempotency for retried writes, and distributed patterns (Saga, TCC, outbox, etc.) only when the problem demands — prefer simpler models when the business allows.
- Observability by default — Structured logs; metrics (latency, errors, saturation); distributed tracing where multi-service; health endpoints that reflect dependencies, not only process up.
- Evolvable design — Avoid premature complexity; keep extension points (strategy, DI, modules). Open/closed where it pays; schema changes additive-first when possible; soft delete / migrations planned.
- Performance & resource awareness — Mind algorithmic cost, memory, network I/O, and DB round-trips. Indexes for real query patterns; caching (local vs distributed) with invalidation story; async where appropriate; connection pools; avoid N+1 and chatty loops.
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 · 123 lines · 52 tokens per session scan A eae0d645dc23
clawteam-rnd-backend is an agent published in the GitHub repository deepelementlab/jupyter-studio (53 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 52 tokens to every session and 1,848 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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