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

references/execution-policy.md

../../references/explore-variant-spec.md

scripts/plan_variants.py

scripts/write_outputs.py