/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?
- Single iteration (/ar:run) — I'll make one change and evaluate
- Start a loop (/ar:loop) — Autonomous with scheduled interval
- 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 .