Experiment Loop Runner

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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Arnie016
• Source: Arnie016/codex-prompt-templates
• License: MIT
• Homepage: https://github.com/Arnie016/codex-prompt-templates

Install and usage instructions live in the source repository linked above.