一键导入
check-experiments
Standardized tmux experiment monitoring with progress parsing, CSV log reading, and status reporting
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Standardized tmux experiment monitoring with progress parsing, CSV log reading, and status reporting
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
计算机论文草稿终审与分层重写工作流。先逻辑后语言:检查全文章节结构 → 章节内部逻辑 → 跨章节一致性 → 写作风格 → 摘要逐句功能 → 图表 → 投稿前 sanity check。问题按 P0/P1/P2 分级。适用于 NeurIPS/ICML/ICLR/KDD/CCS/USENIX/SIGMOD/VLDB/IEEE Transactions 等顶会顶刊。Use when 用户请求论文终审、逻辑诊断、分层重写、投稿前检查,或在草稿已成形阶段进行系统性修改。
Paper editing with built-in style constraints for academic writing quality, including anti-AI detection, LaTeX validation, and figure standards
Address reviewer/editor comments by number with localized, traceable edits. Use when the user provides numbered reviewer concerns, editor proof queries, or revision lists and asks to "address comment N", "fix W1-W4", "respond to editor queries", etc. Complements review-response (rebuttal drafting) and paper-edit (style enforcement).
Use when ingesting arXiv papers into a project-local Zotero library, building a fresh Zotero collection tree for a research project, the Zotero free-tier 300 MB cloud quota is full ("File would exceed quota" / 413 Request Entity Too Large on attachment upload), the user wants PDFs to live alongside project files rather than in Zotero cloud, or the user asks to "把论文加进 Zotero / 建立 Zotero 分类 / set up related_work folder with Zotero".
Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
从论文中提取图片,优先从arXiv源码包获取真正的论文图
| name | check-experiments |
| description | Standardized tmux experiment monitoring with progress parsing, CSV log reading, and status reporting |
| tags | ["Experiment","Monitoring","tmux","ML"] |
Standardized workflow for checking all running ML experiments via tmux sessions.
tmux ls
Report each session: name, creation time, attached/detached status.
For each session, capture the last 30-50 lines:
tmux capture-pane -t <session-name> -p | tail -50
Look for common progress indicators in the output:
Epoch X/Y or epoch: XStep X/Y or X/Y iterationsXX.X%loss: X.XXXX, val_loss: X.XXXXaccuracy, f1, aucIf experiments write to CSV logs, read the latest entries:
# Find recent CSV logs
find . -name "*.csv" -newer <start-time> -type f
# Read last few rows
tail -5 <log-file.csv>
Scan output for common failure patterns:
CUDA out of memoryRuntimeErrorTracebackErrorKilled or OOMOutput a structured report:
## Experiment Status Report
| Session | Status | Progress | Metric | ETA |
|---------|--------|----------|--------|-----|
| sweep-lr | Running | 45/100 epochs | loss: 0.234 | ~2h |
| ablation-1 | Running | 78% | acc: 0.891 | ~30min |
| baseline | Completed | 100/100 | loss: 0.198 | - |
| sweep-wd | Error | 23/100 | CUDA OOM | - |
If experiments.md exists in the project, update it with:
/check-experiments commandexperiment-monitor.js hook shows brief summary at session startstop-summary.js hook shows running session count at session end