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explore-run

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by lllllllama · part of lllllllama/rigorpilot-skills

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, co

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🧩 One of 7 skills in the lllllllama/rigorpilot-skills package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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.