| name | status |
| description | Implement — Show experiment dashboard with results, active loops, and progress. |
| command | /ar:status |
| executor | LLM_BEHAVIOR |
| skill_id | engineering.cs_engineering.autoresearch_agent.status |
| status | ADOPTED |
| security | {"level":"standard","pii":false,"approval_required":false} |
| anchors | ["engineering","visualization","research"] |
| tier | 2 |
| input_schema | [{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}] |
| output_schema | [{"name":"result","type":"string","description":"Primary output from status"}] |
/ar:status — Experiment Dashboard
Show experiment results, active loops, and progress across all experiments.
Usage
/ar:status # Full dashboard
/ar:status engineering/api-speed # Single experiment detail
/ar:status --domain engineering # All experiments in a domain
/ar:status --format markdown # Export as markdown
/ar:status --format csv --output results.csv # Export as CSV
What It Does
Single experiment
python {skill_path}/scripts/log_results.py --experiment {domain}/{name}
Also check for active loop:
cat .autoresearch/{domain}/{name}/loop.json 2>/dev/null
If loop.json exists, show:
Active loop: every {interval} (cron ID: {id}, started: {date})
Domain view
python {skill_path}/scripts/log_results.py --domain {domain}
Full dashboard
python {skill_path}/scripts/log_results.py --dashboard
For each experiment, also check for loop.json and show loop status.
Export
python {skill_path}/scripts/log_results.py --dashboard --format csv --output {file}
python {skill_path}/scripts/log_results.py --dashboard --format markdown --output {file}
Output Example
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 —
Why This Skill Exists
Implement — Show experiment dashboard with results, active loops, and progress.
When to Use
Use this skill when the task requires status capabilities.
What If Fails
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.