Resume

What This Skill Does

The /ar:resume skill resumes a paused or context-limited experiment, allowing users to continue their work without losing progress. It loads the full context of the experiment and provides a summary of the current state before prompting for the next action.

When to Use

• Resuming a paused experiment.
• Continuing an experiment with limited context.
• Reviewing the history of an experiment.
• Picking up where you left off in an optimization process.

Key Features

• Lists available experiments for the user to choose.
• Loads the full context of the experiment.
• Reports the current state of the experiment.
• Asks the user how they would like to continue.

Manual Installation

Usage

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

What It Does

Step 1: List experiments if needed

If no experiment specified:

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

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

Step 2: Load full context

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

Step 3: Report current state

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)

Step 4: Ask next action

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./ar:resume — Resume Experiment

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

Usage

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

What It Does

Step 1: List experiments if needed

If no experiment specified:

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

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

Step 2: Load full context

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

Step 3: Report current state

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)

Step 4: Ask next action

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.