Loop

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.

Published by @Alireza Rezvani·0 agent reads / 30d·0 saves·

/ar:loop — Autonomous Experiment Loop

Start a recurring experiment loop that runs at a user-selected interval.

Usage

/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 loop

What It Does

Step 1: Resolve experiment

If no experiment specified, list experiments and let user pick.

Step 2: Select interval

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:

IntervalCron ExpressionShorthand
10 minutes*/10 * * * *10m
1 hour7 * * * *1h
Daily57 8 * * *daily
Weekly57 8 * * 1weekly
Monthly57 8 1 * *monthly

Step 3: Create the recurring job

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}

Step 4: Store loop metadata

Write to .autoresearch/{domain}/{name}/loop.json:

{
  "cron_id": "{id from CronCreate}",
  "interval": "{user selection}",
  "started": "{ISO timestamp}",
  "experiment": "{domain}/{name}"
}

Step 5: Confirm to user

Loop started for {domain}/{name}
  Interval: {interval description}
  Cron ID: {id}
  Auto-expires: 3 days (CronCreate limit)

  To check progress: /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.

Stopping a Loop

When user runs /ar:loop stop {experiment}:

  1. Read .autoresearch/{domain}/{name}/loop.json to get the cron ID
  2. Call CronDelete with that ID
  3. Delete loop.json
  4. Confirm: "Loop stopped for {experiment}. {n} experiments completed."

Important Limitations

  • 3-day auto-expiry: CronCreate jobs expire after 3 days. For longer experiments, the user must re-run /ar:loop to restart. Results persist — the new loop picks up where the old one left off.
  • One loop per experiment: Don't start multiple loops for the same experiment.
  • Concurrent experiments: Multiple experiments can loop simultaneously ONLY if they're on different git branches (which they are by default — each experiment gets autoresearch/{domain}/{name}).

Bundled with this artifact

1 file

Reference files that ship alongside this artifact. Agents pull these in only when the task needs them.

More on the bench

SKILL0

Cs Scrape

Route, extract, and validate a scraping job (URL or local file) via the universal-scraping-architect skill — refuses to deliver unvalidated data.

software-engineering+2
0
SKILL0

Senior Data Scientist

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

data-science-ml+2
0
SKILL0

Universal Scraping Architect

Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy data pipelines using Firecrawl or local Python scripts.

software-engineering+2
0