Explore Run

# explore-run

Use this as the Rigor Improve / Rigor Explore run leaf skill. The installed slug remains `explore-run` for compatibility.

Use the shared operating principles in `../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide candidate run planning while preserving model judgment about the active repo.

## 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. - It should label run results as bounded evidence and explain when a comparison is not directly fair.

## 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/SCIENTIFIC_CHANGELOG.md` - `explore_outputs/COMPARABILITY_REPORT.md` - `explore_outputs/TOP_RUNS.md` - `explore_outputs/status.json`

## Notes

Use `references/execution-policy.md`, `../ai-research-reproduction/references/explore-variant-spec.md`, `../ai-research-reproduction/references/deep-learning-experiment-principles.md`, `scripts/plan_variants.py`, and `scripts/write_outputs.py`.