| name | experiment-loop-runner |
| description | 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`.
|
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
- Think: read brief, constraints, current best, and recent memory.
- Execute: make one bounded change or launch one run.
- Verify: run the agreed test/benchmark/dry-run.
- Reflect: record result, decision, and next branch.
- 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.
