Ddev Clean

AI Slop Cleaner Skill

Reduce AI-generated slop with a regression-tests-first, smell-by-smell cleanup workflow that preserves behavior and raises signal quality.

When to Use

Use this skill when:

• A code path works but feels bloated, noisy, repetitive, or over-abstracted
• A user asks to “cleanup”, “refactor”, or “deslop” AI-generated output
• Follow-up implementation left duplicate code, dead code, weak boundaries, missing tests, or unnecessary wrapper layers
• You need a disciplined cleanup workflow without broad rewrites

GPT-5.4 Guidance Alignment

• Keep outputs concise and evidence-dense unless risk or the user requests more detail.
• Treat newer user instructions as local workflow updates without discarding earlier non-conflicting constraints.
• Keep using inspection, tests, diagnostics, and verification until the cleanup is grounded.
• Proceed automatically through clear, reversible cleanup steps; ask only when a choice materially changes scope or behavior.

Scoped File Lists and Ralph Workflow

• This skill can accept a file list scope instead of a whole feature area.
• When the caller provides a changed-files list (for example, Ralph session-owned edits), keep the cleanup strictly bounded to those files.
• In the Ralph workflow, the mandatory deslop pass should run this skill on Ralph's changed files only, in standard mode unless the caller explicitly requests otherwise.

与 ddev-gate 的关系

• ddev-clean 不再是 ddev-gate 的独立流水线阶段；清理项识别已并入 gate 的代码评审 subagent（只出清单，只读）。
• 清理执行由主 agent 在收到代码评审结论后按需进行：加载本 skill 直接执行，或派发受限清理 subagent。范围是代码评审给定的 changed-files 或更窄的文件列表。
• 只有在完成编码规范 / 质量 / 注释审查结论之后才执行清理，避免边审边改。
• 目标是在不改变文档已批准的架构、detail、数据流、接口和行为的前提下，删除 slop 并提升可维护性。
• 如果为锁定行为必须补测试，只允许纳入锁定既有行为所必需的最小测试文件，不得借此扩大实现范围。
• 清理产生任何代码修改后，必须补充验证证据；主 agent 必须基于最终代码重新并行派发一致性审查与代码评审做只读复审。
• 如果维护性问题真正需要架构或流程变更才能解决，停止并上报，不要借 cleanup 做重构。

Procedure

1. Lock behavior with regression tests first

   • Identify the behavior that must not change
   • Add or run targeted regression tests before editing cleanup candidates
   • If behavior is currently untested, create the narrowest test coverage needed first

2. Create a cleanup plan before code

   • List the specific smells to remove
   • Bound the pass to the requested files/scope
   • If a file list scope is provided, keep the pass restricted to that changed-files list
   • Order fixes from safest/highest-signal to riskiest
   • Do not start coding until the cleanup plan is explicit

3. Categorize issues before editing

   • Duplication — repeated logic, copy-paste branches, redundant helpers
   • Dead code — unused code, unreachable branches, stale flags, debug leftovers
   • Needless abstraction — pass-through wrappers, speculative indirection, single-use helper layers
   • Boundary violations — hidden coupling, leaky responsibilities, wrong-layer imports or side effects
   • Missing tests — behavior not locked, weak regression coverage, gaps around edge cases

4. Execute passes one smell at a time

   • Pass 1: Dead code deletion
   • Pass 2: Duplicate removal
   • Pass 3: Naming/error handling cleanup
   • Pass 4: Test reinforcement
   • Re-run targeted verification after each pass
   • Avoid bundling unrelated refactors into the same edit set

5. Run quality gates

   • Regression tests stay green
   • Lint passes
   • Typecheck passes
   • Relevant unit/integration tests pass
   • Static/security scan passes when available
   • Diff stays minimal and scoped
   • No new abstractions or dependencies unless explicitly required

6. Finish with an evidence-dense report

   • Changed files
   • Simplifications made
   • Tests/diagnostics/build checks run
   • Remaining risks
   • Residual follow-ups or consciously deferred cleanup

Output Format

AI SLOP CLEANUP REPORT
======================

Scope: [files or feature area]
Behavior Lock: [targeted regression tests added/run]
Cleanup Plan: [bounded smells and order]

Passes Completed:
1. Pass 1: Dead code deletion - [concise fix]
2. Pass 2: Duplicate removal - [concise fix]
3. Pass 3: Naming/error handling cleanup - [concise fix]
4. Pass 4: Test reinforcement - [concise fix]

Quality Gates:
- Regression tests: PASS/FAIL
- Lint: PASS/FAIL
- Typecheck: PASS/FAIL
- Tests: PASS/FAIL
- Static/security scan: PASS/FAIL or N/A

Changed Files:
- [path] - [simplification]

Remaining Risks:
- [none or short deferred item]

Scenario Examples

Good: The user says continue after tests already lock behavior and the next smell pass is clear. Continue with the next bounded cleanup pass.

Good: The user narrows the scope to a specific file after planning. Keep the regression-tests-first workflow, but apply the new scope locally.

Bad: Start rewriting architecture before protecting behavior with tests.

Bad: Collapse multiple smell categories into one large refactor with no intermediate verification.

Gate 审查模式

当本 skill 被 ddev-gate 流程在代码评审返回后加载时，必须使用 reviewer-prompt.md 作为任务模板执行受限清理。该模板定义了清理输入、scope 约束、regression-tests-first 规则和输出格式。清理完成后，主 agent 会基于最终代码重新并行派发一致性审查与代码评审做只读复审。

进度记录

当本 skill 被 ddev-exec 或 ddev-gate 工作流调用时，清理完成后须将 diff 摘要写入项目根目录的 progress.md：

• 记录清理前后的变更文件数和行数（新增/删除）
• 记录各 Pass 通过的 Quality Gates 结果（PASS/FAIL）
• 如有 defer 的风险项，一并记录
• 写到最后一次执行日志后，追加时间戳