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.
npx agentmods add skills/sisibeloved/cpython-optimize-skill/cinderx-remote-lab-opsnpx skills add sisibeloved/cpython-optimize-skill --skill cinderx-remote-lab-opsgit clone --depth 1 https://github.com/sisibeloved/cpython-optimize-skillWrote 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/skills/sisibeloved/cpython-optimize-skill/cinderx-remote-lab-ops)<a href="https://agentmods.dev/skills/sisibeloved/cpython-optimize-skill/cinderx-remote-lab-ops"><img src="https://agentmods.dev/badge/skills/sisibeloved/cpython-optimize-skill/cinderx-remote-lab-ops.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00551 |
| Opus 5 | $0.00026 | $0.00275 |
| Sonnet 5 | $0.00011 | $0.00110 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
cinderx-remote-lab-ops scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
CinderX Remote Lab Ops
远端操作必须服务于 CPython/CinderX lab，不是通用 SSH 技巧。
标准对象
- workspace：当前 Agent 独立宿主机目录。
- tmux：按任务固定 session/window/pane。
- docker compose：只操作
cinderx-test/cpython-baseline。 - logs：每个构建、安装、测试、benchmark 都有日志路径。
输出契约
每条远程命令第一次运行就记录真实命令、stdout/stderr、exit status、日志路径和 tmux pane。长任务必须有 timeout 或进度检查策略。
set -o pipefail
<command> 2>&1 | tee logs/<task>.log
status=${PIPESTATUS[0]}
printf '\n[exit status=%s]\n' "$status"
exit "$status"
异常处理
- 无输出：查进程、tmux capture-pane、日志、CPU/IO/磁盘。
- 网络慢：查 DNS、代理、pip mirror、git 连接和 cache。
- 容器内缺少
gdb、rg/ripgrep、strace、perf、binutils等排障工具时，先读取../using-cpython-optimize/references/container-tooling-guidance.md，探测网络、包管理器、镜像源和 cache，再决定补装；不要直接绕开关键取证路径。 - 不确定是否继续等待时，询问用户。
不要为了补输出盲目重复构建、安装或 benchmark。
反问 Gate
- 远端命令长时间无新增输出，且进程/日志无法证明正常推进时，询问继续等待、查看交互终端、中止还是换策略。
- pip/git/网络下载异常慢时，询问继续等待、切镜像、复用 cache 或让用户处理网络。
- 补装工具的 metadata refresh 或安装长时间无输出时，及时反馈并询问继续等待、切镜像、复用 cache、上传离线包或中止。
- 要 kill 进程、清理目录、重跑有副作用命令或覆盖日志时，先询问。
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.
- 5d ago First seen · 44 lines · 53 tokens per session scan A 00132f382d6d
cinderx-remote-lab-ops is a skill published in the GitHub repository sisibeloved/cpython-optimize-skill (2 stars, last pushed 7d ago), licensed MIT. It adds 53 tokens to every session and 551 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 2026-08-31.
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