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`.