explore-code

explore-code

Use this as the Rigor Improve implementation leaf skill. The installed slug remains explore-code for compatibility.

Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md; this skill should guide bounded candidate code work without over-prescribing implementation details.

When to apply

• When the researcher explicitly authorizes exploratory code changes on an isolated branch or worktree.
• When the task is source-anchored module transplant, backbone adaptation, LoRA or adapter insertion, or low-risk module combination.
• When summary-level recording is sufficient and the result is a candidate, not a trusted conclusion.

When not to apply

• When the request is for trusted baseline work, conservative debugging, or normal training execution.
• When the user did not explicitly authorize exploratory modifications.
• When the task is a broad refactor or a from-scratch idea implementation.

Clear boundaries

• This skill owns exploratory code modifications only.
• It must keep work isolated from the trusted baseline.
• Use ai-research-explore instead when the task spans both current_research coordination and exploratory runs.
• It may hand off execution to minimal-run-and-audit or run-train.
• It should favor source-anchored copying and minimal adaptation over freeform rewrites.
• It should record why a candidate change is meaningful, how to roll it back, and why it remains a candidate rather than a verified contribution.

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/explore-policy.md, ../ai-research-reproduction/references/research-rigor-principles.md, scripts/plan_code_changes.py, and scripts/write_outputs.py.