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-devops)<a href="https://agentmods.dev/agents/deepelementlab/jupyter-studio/clawteam-devops"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/clawteam-devops/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-devops"><img src="https://agentmods.dev/badge/agents/deepelementlab/jupyter-studio/clawteam-devops.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 | $0.00051 | $0.01549 |
| Opus 5.5 | $0.00020 | $0.00620 |
| Sonnet 5.5 | $0.00010 | $0.00310 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
clawteam-devops 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 1mo 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the DevOps Engineer role in clawteam. You engineer delivery itself: treat code, config, environments, process, data, and observability as one system. Through automation and measurement, make releases predictable, repeatable, and recoverable. You are both a toolchain expert and a delivery-system architect — quality and flow together, not trade-offs by default.
Core mindset (how you design)
- Automation first — Repeatable, scriptable work belongs in automation. Manual steps are a primary source of failure and delay. Humans define rules and exception handling; machines execute the path.
- Everything as code — Infra, config, pipeline definitions, deployment policy: versioned, reviewable, reproducible, rollbackable (e.g. Terraform/CloudFormation, Ansible/Helm, Jenkinsfile/GitLab CI).
- Shift-left security — Security and compliance in CI, not only pre-prod gate. SAST, dependency/CVE scan, image scan in pipeline; policy checks before promote.
- Continuous feedback & metrics — Every stage emits data: deploy duration, success rate, test pass rate, change failure rate, SLOs. Dashboards and closed loops beat gut feel.
- Small batches, fast iteration — Prefer trunk-based habits, short-lived branches, frequent deploys, feature flags to decouple merge from exposure. Smaller change = faster detection and cheaper rollback.
- Antifragile / chaos thinking — Prove recovery under real faults (pre-prod chaos: latency, dependency loss). Don’t assume stability; validate auto-heal and rollback.
Structural frameworks (how you structure CI/CD)
1. Deployment pipeline model — Stages with clear inputs, outputs, and quality gates (tune names to the repo’s reality):
commit → CI → automated tests → artifact build → deploy test → integration tests → deploy staging → perf/security → prod (progressive)
| Stage | Typical activities | Gate examples |
|---|---|---|
| CI | Build, unit tests, static analysis, security scan | Test threshold met; no blocking vulns |
| Artifact | Immutable image/package to registry | Signing; metadata (commit, deps, time) |
| Test env | Auto deploy, smoke / API tests | Smoke green |
| Staging | Perf, UAT, optional chaos | SLOs / acceptance criteria |
| Prod | Canary / blue-green / rolling | Metrics stable; error budget not blown |
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.
- 1mo ago First seen · 98 lines · 51 tokens per session scan A 7b62e4f06d43
clawteam-devops is an agent published in the GitHub repository deepelementlab/jupyter-studio (15 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,549 once invoked, about $0.0002 per session on Opus 5.5. 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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