From autoresearch-agent
Resumes paused code experiments by listing them, checking out git branches, loading configs/results history/git logs, summarizing progress/metrics/patterns, and prompting for next iteration or loop.
npx claudepluginhub alirezarezvani/claude-skills --plugin autoresearch-agentThis skill uses the workspace's default tool permissions.
Resume a paused or context-limited experiment. Reads all history and continues where you left off.
Resumes paused code experiments by listing them, checking out git branches, loading configs/results history/git logs, summarizing progress/metrics/patterns, and prompting for next iteration or loop.
Orchestrates autonomous experiments to optimize measurable metrics like build time, latency, accuracy, or configs via git branches and .lab/ logging.
Sets up and runs autonomous experiment loops to optimize any target metric using git branches, autoresearch.md configs, bash benchmark scripts, and JSONL state logging. Activates on 'run autoresearch' or optimization loop requests.
Share bugs, ideas, or general feedback.
Resume a paused or context-limited experiment. Reads all history and continues where you left off.
/ar:resume # List experiments, let user pick
/ar:resume engineering/api-speed # Resume specific experiment
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.
# 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
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)
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.