accountable-engineerin…
Use explicit human checkpoints for architecture, security, rollout, or operational decisions.
Audit a diff for structural maintainability and unnecessary complexity.
$ npx -y skills add jellydn/my-ai-tools --skill code-quality-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/code-quality-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Audit a diff for structural maintainability and unnecessary complexity.
name: code-quality-review description: "Audit a diff for structural maintainability and unnecessary complexity." license: MIT compatibility: cline, claude, opencode, amp, codex, gemini, cursor, pi hint: Use for a deep review of maintainability, abstraction quality, and avoidable complexity user-invocable: true disable-model-invocation: true metadata:
Review the current branch diff for structural quality. Preserve behavior, but actively look for a simpler design that deletes concepts, branches, wrappers, or layers instead of moving complexity around.
Check each meaningful change for:
1. **Simpler structure** — Can a different model or ownership boundary remove branches, modes, helpers, or layers? 2. **Cohesive control flow** — Flag scattered special cases, repeated conditions, deep nesting, and mixed responsibilities. 3. **Useful abstractions** — Flag thin wrappers, generic magic, speculative extension points, and duplicate helpers. 4. **Clean boundaries** — Keep feature logic in its canonical module and make types and invariants explicit. 5. **Reasonable file size** — Treat a change that pushes a file above 1,000 lines as a decomposition signal unless the file has a clear structural reason to stay whole. 6. **Sound orchestration** — Flag unnecessarily sequential independent work and updates that can leave related state partially applied when a clearer atomic design exists. 7. **Legibility** — Prefer direct, boring code with clear names and comments that explain only non-obvious reasons.
Report a finding only when you can name:
Treat these as blockers unless the implementation has a clear justification:
Do not block on personal style, hypothetical future needs, or a rewrite that is only differently complex.
Order findings by impact:
1. Structural regressions and simpler designs 2. Control-flow, boundary, abstraction, and type problems 3. File-size, modularity, and legibility concerns
For each finding, use:
[severity] file:line — finding Impact: concrete cost or risk Fix: smallest practical remedy
End with `APPROVE` when no blocking finding remains, or `CHANGES REQUESTED` with the blocker count. Be direct and respectful.
Portable, source-controlled configuration for AI coding assistants. my-ai-tools installs one consistent set of agent guidelines, skills, commands, hooks, MCP servers, and tool settings across supported AI coding assistants.
Use explicit human checkpoints for architecture, security, rollout, or operational decisions.
Record architecture decisions with ADRs — captures why, alternatives considered, and consequences
Use when monitoring an open GitHub PR for CI failures, review feedback, mergeability, and safe retries or fixes.
Inspect history and architecture for hidden constraints before risky cross-cutting changes.
Build an interactive report or experiment when the user asks to explore model capabilities.
Review a branch, PR, or worktree diff for repository conventions and stated intent.