关于 resume
用于恢复暂停或受上下文限制的 autoresearch 实验。它会读取配置与历史记录、汇报当前状态,并提供单次运行、循环运行或查看结果等后续选项。
主要功能
- 01列出实验及其状态
- 02读取实验配置与历史
- 03汇总指标和近期结果
- 04提供单次或循环运行选项
适用场景
- 01暂停后继续实验
- 02决定下一步前查看结果
安装
复制下面的命令,在终端中运行。
$
npx skills add https:/ / github.com/ alirezarezvani/ claude-skills --skill resume安装后在 Claude Code 中输入/resume确认已加载。
详细介绍
/ar:resume — 恢复实验
恢复已暂停或因上下文受限而中断的实验。读取全部历史记录,从中断处继续。
用法
/ar:resume # List experiments, let user pick
/ar:resume engineering/api-speed # Resume specific experiment
功能说明
第 1 步:如未指定则列出实验
如果未指定实验:
python {skill_path}/scripts/setup_experiment.py --list
根据 results.tsv 的修改时间显示每个实验的状态(活跃/已暂停/已完成)。由用户选择。
第 2 步:加载完整上下文
# Checkout the experiment branch
git checkout autoresearch/{domain}/{name}
# Read config
cat .autoresearch/{domain}/{name}/config.cfg
# Read strategy
cat .autoresearch/{domain}/{name}/program.md
# Read full results history
cat .autoresearch/{domain}/{name}/results.tsv
# Read recent git log for the branch
git log --oneline -20
第 3 步:报告当前状态
向用户总结:
Resuming: engineering/api-speed
Target: src/api/search.py
Metric: p50_ms (lower is better)
Experiments: 23 total — 8 kept, 12 discarded, 3 crashed
Best: 185ms (-42% from baseline of 320ms)
Last experiment: "added response caching" → KEEP (185ms)
Recent patterns:
- Caching changes: 3 kept, 1 discarded (consistently helpful)
- Algorithm changes: 2 discarded, 1 crashed (high risk, low reward so far)
- I/O optimization: 2 kept (promising direction)
第 4 步:询问下一步操作
How would you like to continue?
1. Single iteration (/ar:run) — I'll make one change and evaluate
2. Start a loop (/ar:loop) — Autonomous with scheduled interval
3. Just show me the results — I'll review and decide
如果用户选择 loop,则将实验预先选定后交给 /ar:loop。
如果选择 single,则交给 /ar:run。
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