resume

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Original

English

🇨🇳

Translation

Chinese

/ar:resume — Resume Experiment

/ar:resume — 恢复实验

Resume a paused or context-limited experiment. Reads all history and continues where you left off.

恢复已暂停或受上下文限制的实验。读取所有历史记录，从上次中断的位置继续。

Usage

使用方法

/ar:resume                                  # List experiments, let user pick
/ar:resume engineering/api-speed            # Resume specific experiment

/ar:resume                                  # 列出实验，让用户选择
/ar:resume engineering/api-speed            # 恢复指定实验

What It Does

功能说明

Step 1: List experiments if needed

步骤1：必要时列出实验

If no experiment specified:

bash

python {skill_path}/scripts/setup_experiment.py --list

Show status for each (active/paused/done based on results.tsv age). Let user pick.

如果未指定实验：

bash

python {skill_path}/scripts/setup_experiment.py --list

显示每个实验的状态（根据results.tsv的时间标记为活跃/已暂停/已完成），让用户选择。

Step 2: Load full context

步骤2：加载完整上下文

bash

undefined

bash

undefined

Checkout the experiment branch

切换到实验分支

git checkout autoresearch/{domain}/{name}

Read config

读取配置文件

cat .autoresearch/{domain}/{name}/config.cfg

Read strategy

读取策略文件

cat .autoresearch/{domain}/{name}/program.md

Read full results history

读取完整结果历史

cat .autoresearch/{domain}/{name}/results.tsv

Read recent git log for the branch

读取该分支的近期git日志

git log --oneline -20

undefined

git log --oneline -20

undefined

Step 3: Report current state

步骤3：报告当前状态

Summarize for the user:

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)

为用户总结：

正在恢复：engineering/api-speed
  目标文件：src/api/search.py
  指标：p50_ms（数值越低越好）
  实验总数：23次 — 保留8次，丢弃12次，失败3次
  最佳结果：185ms（相比基线320ms提升42%）
  上一次实验："添加响应缓存" → 保留（185ms）

  近期模式：
  - 缓存变更：3次保留，1次丢弃（持续有效）
  - 算法变更：2次丢弃，1次失败（高风险，目前回报低）
  - I/O优化：2次保留（方向可行）

Step 4: Ask next action

步骤4：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

If the user picks loop, hand off to

/ar:loop

with the experiment pre-selected. If single, hand off to

/ar:run

您希望如何继续？
  1. 单次迭代（/ar:run） — 我将进行一次变更并评估
  2. 启动循环（/ar:loop） — 按计划间隔自动运行
  3. 仅显示结果 — 我将先查看再决定

如果用户选择循环模式，将自动切换到

/ar:loop

并预先选中该实验。如果选择单次迭代，将切换到

/ar:run

。