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Run a short, bounded autonomous loop of debugging or experiments before checking back in. Use when the user wants the agent to iterate for a few cycles, keep going autonomously, or stop only after a fixed budget or explicit stop rule is met.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | bounded-auto-loop |
| description | Run a short, bounded autonomous loop of debugging or experiments before checking back in. Use when the user wants the agent to iterate for a few cycles, keep going autonomously, or stop only after a fixed budget or explicit stop rule is met. |
Use this skill when the user wants a few autonomous iterations without an open-ended research spiral.
Before starting, lock these down:
If any of these are missing, infer the narrowest reasonable version and state it briefly.
For each iteration:
Do not queue multiple speculative branches at once unless they are independent and the stop rule still stays sharp.
Stop early if:
Do not consume the full iteration budget just because it exists.
When the loop stops, report:
Keep the report decision-focused. Do not dump every intermediate detail unless it changed the conclusion.
Design the smallest useful experiment matrix that separates important hypotheses and changes a decision. Use when the agent is proposing runs, ablations, sweeps, benchmarks, or debugging experiments across research or engineering projects.
Create or revise PI-, collaborator-, or lab-facing scientific progress updates, slide narratives, and research briefs that connect goals, evidence, caveats, and decisions. Use when an agent must synthesize experiments for a scientific audience, prepare a lab meeting or progress review, or turn results into a decision-focused story.
Check whether findings make sense before trusting or reporting them by comparing to the nearest reference, asking what mechanism could explain them, and naming a falsifier. Use when the agent is interpreting results, summarizing progress, assessing regressions, or deciding whether an observation should change the plan.
Inspect queue state, submit or cancel jobs, debug sbatch and submitit runs, and separate scheduler issues from runtime failures. Use when the agent works with Slurm commands, submit scripts, submit.py launchers, or submitit logs.
Directly monitor long-running jobs or pipelines, classify them as healthy, waiting, stalled, failed, completed, or unknown, and choose the next action. Use when the active agent needs to babysit training runs, batch jobs, services, experiments, or scheduled work over time.
Run safe, high-yield codebase maintenance passes that trim dead code, vestiges, duplicate implementations, stale scripts/configs, and excessive LOC without churn. Use when the agent is asked for a maintenance pass, cleanup pass, code cleanup, slop trimming, dead-code removal, de-duplication, vestige pruning, or refactors whose purpose is reducing maintenance burden rather than adding features.
