loop

# /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: | 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` | ### 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`: ```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}`). --- ## Why This Skill Exists Implement — Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. <!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. --> ## When to Use Use this skill when the task requires loop capabilities. <!-- SR_40: auto-generated from frontmatter `when`/`description` (OPP-Phase3). --> ## 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. <!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->