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Get Started Free →Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. Use when the user runs /ar:loop or asks to run an autoresearch experiment continuously on a schedule.
.claude/skills/alirezarezvani-loop/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 169% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 15% | 0% |
Start a recurring experiment loop that runs at a user-selected interval.
/ar:loop engineering/api-speed # Start loop (prompts for interval)
/ar:loop engineering/api-speed 10m # Every 10 minutes
/ar:loop engineering/api-speed 1h # Every hour
/ar:loop engineering/api-speed daily # Daily at ~9am
/ar:loop engineering/api-speed weekly # Weekly on Monday ~9am
/ar:loop engineering/api-speed monthly # Monthly on 1st ~9am
/ar:loop stop engineering/api-speed # Stop an active loopIf no experiment specified, list experiments and let user pick.
If interval not provided as argument, present options:
Select loop interval:
1. Every 10 minutes (rapid — stay and watch)
2. Every hour (background — check back later)
3. Daily at ~9am (overnight experiments)
4. Weekly on Monday (long-running experiments)
5. Monthly on 1st (slow experiments)Map to cron expressions:
| Interval | Cron Expression | Shorthand | |----------|----------------|-----------| | 10 minutes | */10 * * * * | 10m | | 1 hour | 7 * * * * | 1h | | Daily | 57 8 * * * | daily | | Weekly | 57 8 * * 1 | weekly | | Monthly | 57 8 1 * * | monthly |
Use CronCreate with this prompt (fill in the experiment details):
You are running autoresearch experiment "{domain}/{name}".
1. Read .autoresearch/{domain}/{name}/config.cfg for: target, evaluate_cmd, metric, metric_direction
2. Read .autoresearch/{domain}/{name}/program.md for strategy and constraints
3. Read .autoresearch/{domain}/{name}/results.tsv for experiment history
4. Run: git checkout autoresearch/{domain}/{name}
Then do exactly ONE iteration:
- Review results.tsv: what worked, what failed, what hasn't been tried
- Edit the target file with ONE change (strategy escalation based on run count)
- Commit: git add {target} && git commit -m "experiment: {description}"
- Evaluate: python {skill_path}/scripts/run_experiment.py --experiment {domain}/{name} --single
- Read the output (KEEP/DISCARD/CRASH)
Rules:
- ONE change per experiment
- NEVER modify the evaluator
- If 5 consecutive crashes in results.tsv, delete this cron job (CronDelete) and alert
- After every 10 experiments, update Strategy section of program.md
Current best metric: {read from results.tsv or "no baseline yet"}
Total experiments so far: {count from results.tsv}Write to .autoresearch/{domain}/{name}/loop.json:
json{ "cron_id": "{id from CronCreate}", "interval": "{user selection}", "started": "{ISO timestamp}", "experiment": "{domain}/{name}" }
Loop started for {domain}/{name}
Interval: {interval description}
Cron ID: {id}
Auto-expires: 3 days (CronCreate limit)
To check progress: /ar:ar-status
To stop the loop: /ar:loop stop {domain}/{name}
Note: Recurring jobs auto-expire after 3 days.
Run /ar:loop again to restart after expiry.When user runs /ar:loop stop {experiment}:
.autoresearch/{domain}/{name}/loop.json to get the cron IDCronDelete with that IDloop.json/ar:loop to restart. Results persist — the new loop picks up where the old one left off.autoresearch/{domain}/{name}).| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,696 | 15,697 | +7% | 1 | 1 | 0% | 1,791 | 1,409 | -21% | 0 | 0 | — |
case-02 | fail→fail | 8,370 | 16,597 | +98% | 1 | 1 | 0% | 561 | 1,493 | +166% | 0 | 0 | — |
case-03 | fail→fail | 15,341 | 16,353 | +7% | 1 | 1 | 0% | 1,625 | 1,514 | -7% | 0 | 0 | — |
case-04 | fail→pass | 8,222 | 7,632 | -7% | 1 | 1 | 0% | 557 | 1,501 | +169% | 0 | 0 | — |
case-05 | fail→pass | 12,384 | 7,132 | -42% | 1 | 1 | 0% | 1,187 | 1,477 | +24% | 0 | 0 | — |
case-06 | fail→pass | 10,537 | 6,618 | -37% | 1 | 1 | 0% | 955 | 1,380 | +45% | 0 | 0 | — |
case-07 | fail→pass | 10,774 | 7,094 | -34% | 1 | 1 | 0% | 1,053 | 1,435 | +36% | 0 | 0 | — |
case-17 | fail→pass | 13,087 | 3,374 | -74% | 1 | 1 | 0% | 1,520 | 1,755 | +15% | 0 | 0 | — |
case-08 | pass→pass | 8,135 | 6,497 | -20% | 1 | 1 | 0% | 446 | 1,348 | +202% | 0 | 0 | — |
case-09 | fail→pass | 18,064 | 7,289 | -60% | 1 | 1 | 0% | 2,540 | 1,526 | -40% | 0 | 0 | — |
case-10 | fail→pass | 23,697 | 7,553 | -68% | 1 | 1 | 0% | 3,021 | 1,513 | -50% | 0 | 0 | — |
case-11 | fail→pass | 13,633 | 6,755 | -50% | 1 | 1 | 0% | 1,484 | 1,389 | -6% | 0 | 0 | — |
case-12 | fail→pass | 18,082 | 6,426 | -64% | 1 | 1 | 0% | 1,798 | 1,293 | -28% | 0 | 0 | — |
case-13 | fail→pass | 13,634 | 6,581 | -52% | 1 | 1 | 0% | 1,314 | 1,371 | +4% | 0 | 0 | — |
case-14 | pass→pass | 10,759 | 7,869 | -27% | 1 | 1 | 0% | 899 | 1,566 | +74% | 0 | 0 | — |
case-15 | pass→pass | 21,490 | 9,211 | -57% | 1 | 1 | 0% | 1,546 | 1,532 | -1% | 0 | 0 | — |
case-16 | pass→pass | 18,512 | 10,928 | -41% | 1 | 1 | 0% | 2,174 | 2,703 | +24% | 0 | 0 | — |
case-18 | fail→pass | 16,768 | 7,332 | -56% | 1 | 1 | 0% | 3,019 | 1,495 | -50% | 0 | 0 | — |
case-19 | fail→pass | 13,279 | 7,684 | -42% | 1 | 1 | 0% | 1,512 | 1,592 | +5% | 0 | 0 | — |
case-20 | fail→pass | 11,723 | 6,713 | -43% | 1 | 1 | 0% | 923 | 2,079 | +125% | 0 | 0 | — |
case-21 | fail→fail | 5,619 | 14,297 | +154% | 1 | 1 | 0% | 840 | 2,666 | +217% | 0 | 0 | — |
case-22 | fail→fail | 19,192 | 3,660 | -81% | 1 | 1 | 0% | 2,121 | 1,717 | -19% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +59 percentage points is the difference between those two pass rates over the 19 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/10/2026 | +50% |
Other measured skills in the registry, with their headline benchmark lift.