explore-run

explore-run

When to apply

• When the researcher explicitly authorizes exploratory runs.
• When the task is a small-subset validation, short-cycle training probe, batch sweep, idle-GPU search, or quick transfer-learning trial.
• When the output should rank candidate runs rather than certify trusted success.

When not to apply

• When the user wants trusted training execution or conservative verification.
• When there is no explicit exploratory authorization.
• When the task is repository setup, intake, or debugging.

Clear boundaries

• This skill owns exploratory execution planning and summary only.
• Use ai-research-explore instead when the task spans both current_research coordination and exploratory code changes.
• It may hand off actual command execution to minimal-run-and-audit or run-train.
• It should keep experiment state isolated from the trusted baseline.
• It should prefer small-subset and short-cycle checks before heavier exploratory runs.

Ranking Semantics

• Pre-execution candidate selection uses three factors: cost, success_rate, and expected_gain.
• Default weights should stay conservative unless the researcher explicitly provides selection_weights.
• Budget pruning still applies after scoring through max_variants and max_short_cycle_runs.
• If runs are executed later, downstream ranking should switch to real execution evidence, not stay purely heuristic.

Variant Spec Hints

• Use variant_axes to define the candidate dimension grid.
• Use subset_sizes and short_run_steps to express exploratory run scale.
• Use selection_weights to rebalance cost, success_rate, and expected_gain.
• Use primary_metric and metric_goal so downstream ranking can order executed candidates consistently.

Output expectations

• explore_outputs/CHANGESET.md
• explore_outputs/TOP_RUNS.md
• explore_outputs/status.json

Notes

Use references/execution-policy.md, ../../references/explore-variant-spec.md, scripts/plan_variants.py, and scripts/write_outputs.py.