name: experiment-cycle description: Run or design an experiment with fixed/varying variables, metrics, and recorded results. Use for ablations, evals, or hyperparameter runs.

Experiment cycle

When to use

• User wants to run an experiment (ablation, eval, hyperparameter, comparison).
• Task involves "vary X, measure Y, record Z".

Input/Output

• Input: What varies, what is fixed, metrics, where to record (path or format).
• Output: Command(s) run, where results were written, facts (numbers), inference (optional), recommendations.

Steps (SOP)

1. Define: Fixed variables, varying variables, metrics, acceptance (e.g. run completes, metric threshold).
2. Run: Execute with project scripts if any (e.g. run_ablation.py). Use stated config.
3. Record: Write results to agreed path (file, table). Do not leave only in chat.
4. Conclude: Facts (observed values) | Inference ("likely because...") | Recommendations ("next: ...").

Acceptance criteria

• Results written to a file or artifact. Facts separated from inference. No "improved" without evidence or baseline.

Common failures

• No record: Always write results somewhere reproducible.
• Mixing fact and inference: Label clearly. Do not state "better" without numbers or baseline.
• Under-spec: If config is vague, state assumptions (e.g. seed, single run) and note replication needs.

When not to use

• One-off script with no metrics (use plan-then-implement).
• Bug fix (use debug-regression).
• Feature implementation (use implement-feature-with-gates).