---
id: gh-explore-code
name: "explore-code"
url: https://skills.yangsir.net/skill/gh-explore-code
author: lllllllama
domain: ai-software-architecture-engineering
tags: ["deep-learning", "code-exploration", "model-adaptation", "research-engineering", "auditable-changes"]
install_count: 331700
rating: 4.90 (120 reviews)
github: https://github.com/lllllllama/rigorpilot-skills/tree/main/skills/explore-code
---

# explore-code

> 此技能作为深度学习研究中的严谨改进实现叶技能，支持研究人员在隔离分支上进行探索性代码修改，例如移植模块、调整骨干网络或添加适配器层。它确保所有更改都可审计并可回滚，从而帮助研究人员高效地进行低风险实验，并系统地记录实验过程和结果，提升研究的严谨性和可追溯性。

**Stats**: 331,700 installs · 4.9/5 (120 reviews)

## Before / After 对比

### 深度学习探索性代码修改效率

**Before**:

在没有此技能之前，开发者在深度学习研究中进行探索性代码修改时，需要手动管理实验分支、记录每次修改的细节和潜在的回滚方案。这通常耗费大量时间，且容易因记录不完整导致实验结果难以追溯或回滚失败，严重影响研究效率。

**After**:

该 Skill 能够自动化管理探索性代码修改过程，确保所有更改都在隔离环境中进行，并自动生成可审计的记录和回滚方案。这极大地减少了开发者在管理实验和追踪变更上的时间投入，使他们能更专注于科学发现，并确保实验的可重复性。

| Metric | Before | After | Change |
|---|---|---|---|
| 实验分支管理与变更追踪时间 | 8小时 | 1小时 | -88% |

## Readme

# 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`.



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*Source: https://skills.yangsir.net/skill/gh-explore-code*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-explore-code/markdown*