scriptorium
claudeAcademic research toolkit: paper search, Zotero, LaTeX thesis, experiment tables, HTML slides.
3 skills · 1 agent · 2 MCP servers · hooks
2
claude
trainhub
Multi-platform ML training workflow: dispatch, monitor, harvest across Kaggle / Colab / SSH.
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Plugin trainhub from marketplace shallow-local in Shallow-dusty/claude-plugins. Commands read from the repo's manifest — 1 month ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
What it installs
Skills (4)
train-config配置或查看项目的训练平台默认值（Kaggle/Colab/SSH 远程 GPU）。首次在项目中使用 trainhub 前运行，或要切换默认平台、更新凭据引用、调整指标口径时使用。触发词：配置训练平台、setup training、训练默认值、切平台。
train-harvest训练完成后下载产物，解析指标，归档到 weights/ 目录。接在 train-watch 检测到 COMPLETE 之后。触发词：收实验结果、下载 kernel output、归档 weights、parse results.csv。
train-submit把训练任务派发到目标平台（Kaggle kernel / Colab notebook / SSH 远程主机）。适用：在本地准备好代码 + 配置后，推到远端开跑。触发词：提交训练、push kernel、run on kaggle/colab/ssh、派发实验。
train-watch轮询训练任务状态（Kaggle kernel / SSH tmux session / Colab runtime）。单次查询，不循环。要周期性监控请组合 /loop。触发词：查训练状态、kernel status、实验跑完没、training 进度。
Hooks (1)
- PreToolUse
for f in $CLAUDE_FILE_PATHS; do case "$f" in */weights/*|*/runs/*|*/results/*|*/datasets/*|*/checkpoints/*) echo "BLOCK: trainhub 保护 — 禁止直接修改 weights/ runs/ results/ datasets/ checkpoints/ 下的文件。这些是训练产
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