explore-code

SKILL.md

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
../../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, ../../references/research-rigor-principles.md, scripts/plan_code_changes.py, and scripts/write_outputs.py.