Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

name: explore-run description: Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in explore_outputs/. Do not use for end-to-end exploration orchestration on top of current_research, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation. ---

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.