explore-code
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
适合你，如果需要在研究代码库中做低风险的结构调整并保留回滚记录
npx oh-my-skill add lllllllama/rigorpilot-skills/explore-codecurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- lllllllama/rigorpilot-skills/explore-codenpx oh-my-skill verify lllllllama/rigorpilot-skills/explore-code怎么用
商店整理自技能原文 · 版本 56bc41c · 表述以原文为准装上后，Claude 会在你授权时，在独立分支上修改深度学习代码，比如移植模块、调整骨干网络、添加 LoRA 层或替换输出头，并记录修改原因和回滚方法。
当你明确要求 Claude 在隔离分支上做探索性代码修改（如模块移植、骨干适配、添加 LoRA 层等）时触发。
技能原文 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-exploreinstead when the task spans both current_research coordination and exploratory runs. - It may hand off execution to
minimal-run-and-auditorrun-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.mdexplore_outputs/SCIENTIFIC_CHANGELOG.mdexplore_outputs/COMPARABILITY_REPORT.mdexplore_outputs/TOP_RUNS.mdexplore_outputs/status.json
Notes
Use references/explore-policy.md, ../../references/research-rigor-principles.md, scripts/plan_code_changes.py, and scripts/write_outputs.py.