Show experiment dashboard with results, active loops, and progress. Use when the user runs /ar:ar-status or asks how an autoresearch experiment is going.
68
83%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
—
The risk profile of this skill
Show experiment results, active loops, and progress across all experiments.
/ar:ar-status # Full dashboard
/ar:ar-status engineering/api-speed # Single experiment detail
/ar:ar-status --domain engineering # All experiments in a domain
/ar:ar-status --format markdown # Export as markdown
/ar:ar-status --format csv --output results.csv # Export as CSVpython {skill_path}/scripts/log_results.py --experiment {domain}/{name}Also check for active loop:
cat .autoresearch/{domain}/{name}/loop.json 2>/dev/nullIf loop.json exists, show:
Active loop: every {interval} (cron ID: {id}, started: {date})python {skill_path}/scripts/log_results.py --domain {domain}python {skill_path}/scripts/log_results.py --dashboardFor each experiment, also check for loop.json and show loop status.
# CSV
python {skill_path}/scripts/log_results.py --dashboard --format csv --output {file}
# Markdown
python {skill_path}/scripts/log_results.py --dashboard --format markdown --output {file}DOMAIN EXPERIMENT RUNS KEPT BEST CHANGE STATUS LOOP
engineering api-speed 47 14 185ms -76.9% active every 1h
engineering bundle-size 23 8 412KB -58.3% paused —
marketing medium-ctr 31 11 8.4/10 +68.0% active daily
prompts support-tone 15 6 82/100 +46.4% done —19392f7
Also appears in
since Aug 28, 2026
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.