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Experiment loop runner

Skill Arnie016/codex-prompt-templates/skills/experiment-loop-runner

Color-coded Codex prompt templates and Agent Skills for plugin-orchestrated AI coding workflows, MCP safety, repo intelligence, and automation.

Install
npx -y skills add Arnie016/codex-prompt-templates --skill experiment-loop-runner

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Run a controlled THINK-EXECUTE-REFLECT loop for coding, research, or ML experiments. Use when the user wants iterative improvement, benchmark-driven changes, autonomous experiment planning, low-cost monitoring, or progress reports without uncontrolled agent drift. Skip production release loops that require gateway policy, trace-to-regression evals, guardrail rollout, budgets, or shadow/canary traffic; use `$auto-skill-build-agent-reliability-loop`.

SKILL.md

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Experiment Loop Runner

Use this when progress depends on repeated trials with measurable outcomes.

Required Control Files

PROJECT_BRIEF.md
workspace/
  MEMORY_LOG.md
  HUMAN_DIRECTIVE.md
  runs/

Loop

  1. Think: read brief, constraints, current best, and recent memory.
  2. Execute: make one bounded change or launch one run.
  3. Verify: run the agreed test/benchmark/dry-run.
  4. Reflect: record result, decision, and next branch.
  5. Stop or iterate: continue only when improvement signal exists.

Low-Cost Monitoring

For long jobs, monitor with shell/process/log checks instead of repeatedly asking the model to think:

  • PID alive check
  • tail last log lines
  • GPU/CPU status when relevant
  • metric parser script

Stop Conditions

  • target reached
  • no improvement after N attempts
  • verification becomes flaky
  • human directive changes the objective
  • cost/time budget exceeded

For production agent release loops that combine traces, eval groups, gateway routing, guardrails, budgets, and shadow/canary traffic, use $auto-skill-build-agent-reliability-loop first and use this skill for the bounded experiment cycles inside that plan.

Reference pattern source: sources/auto-deep-researcher-24x7.

Validation

  • Each loop has a stop condition before execution.
  • Each iteration records the change, verification command, result, and next decision.
  • Experiments that affect production users/tools are escalated to the reliability-loop skill.

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