/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 .