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Code review process, effective feedback, PR best practices, team working agreements, review automation, AI-augmented review, and review antipatterns. Use when reviewing code, preparing PRs, giving review feedback, setting up review processes, writing review comments, or conducting code reviews during serve phase.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Antipattern catalog with categorized patterns, symptoms, examples, and fixes. Use when reviewing code for quality issues, detecting bad patterns during plan-audit or scope, evaluating whether a pattern is an antipattern or acceptable tradeoff, or fixing existing code smells. Covers surprise, misuse, complexity, and premature antipattern categories with severity classification.
Naming decisions for variables, functions, classes, constants, and APIs. Use when choosing names, reviewing naming quality, refactoring unclear names, or establishing naming conventions. Covers descriptive naming, naming as documentation, language-specific conventions, and common naming antipatterns.
Plan quality auditing with completeness scorecard, antipattern risk detection, and readiness checks. Use when auditing implementation plans, evaluating plan completeness, checking error handling strategy, assessing naming and readability pre-checks, reviewing testing strategy, evaluating tradeoff documentation, or running plan-audit on a software project. Covers the 10-point completeness scorecard, code quality pre-checks, antipattern risk assessment, and build-readiness decision.
Dead code detection strategies, safe removal processes, dependency pruning, and bloat metrics for systematically reducing codebase dead weight. Use when evaluating codebase health during architecture-audit, performing cleanup during cook, reviewing unused dependencies, removing commented-out code, or measuring code bloat.
Code quality pillars, goals, abstraction layers, and tradeoff thinking. Use when evaluating code quality, setting quality goals, choosing abstraction levels, making design tradeoffs, or auditing code against quality pillars. Covers readability, modularity, testability, reusability, and the principle of least astonishment.
Code readability patterns covering comments, function decomposition, nesting, naming conventions, and code structure. Use when evaluating whether code is easy to understand, deciding when and how to write comments, decomposing functions, reducing nesting depth, improving code structure, or reviewing code for readability. Covers comment strategy, function length, abstraction level mixing, nesting reduction, anonymous function readability, and making bad code visually obvious.
| name | code-review |
| description | Code review process, effective feedback, PR best practices, team working agreements, review automation, AI-augmented review, and review antipatterns. Use when reviewing code, preparing PRs, giving review feedback, setting up review processes, writing review comments, or conducting code reviews during serve phase. |
| Goal | What You're Checking | Time Allocation |
|---|---|---|
| Correctness | Does it work? Edge cases, off-by-ones, null handling | 40% |
| Design | Right abstractions? Fits the architecture? | 25% |
| Security | Auth, input validation, data exposure, injection | 15% |
| Maintainability | Readable? Testable? Future-developer-friendly? | 15% |
| Style | Naming, formatting, conventions | 5% (automate this) |
| Component | Purpose | Example |
|---|---|---|
| Request | What to change | "Can we rename item to something more descriptive?" |
| Rationale | Why it matters | "item is vague and doesn't convey its role in discount calculation." |
| Result | What good looks like | "Something like discountEligibleItem captures the context." |
| Label | Meaning | Author Action |
|---|---|---|
| blocker | Must fix before merge | Address or discuss |
| suggestion | Improvement worth considering | Decide and respond |
| nitpick | Minor style/preference | Optional — take or leave |
| question | Need to understand intent | Explain the reasoning |
| praise | Something done well | Acknowledge (builds trust) |
| Area | Questions to Answer |
|---|---|
| Response time | How quickly should reviews start? (e.g., within 4 business hours) |
| Review depth | What's expected? (checklist-based? architecture-level?) |
| PR size limits | Maximum lines? When must PRs be split? |
| Approval policy | How many approvals to merge? Who can approve? |
| Comment conventions | Which severity labels? Required vs. optional feedback? |
| Automation baseline | What's automated? (linting, formatting, security scans) |
| Escalation | What if reviewer and author disagree? |
| Exceptions | Emergency/hotfix bypass process? |
| Antipattern | Symptom | Fix |
|---|---|---|
| Rubber-stamping | "LGTM" without reading the code | Require specific comment on at least one aspect |
| Nitpicking | 20 comments about spacing, zero about logic | Automate style; focus comments on design |
| Gatekeeping | One person blocks all merges with personal preferences | TWA defines objective standards; rotate reviewers |
| Ghost reviewing | Review assigned, no response for days | SLA with escalation; review rotation |
| Bike-shedding | Lengthy debate on trivial decisions | Time-box discussions; "author decides" for preferences |
| Drive-by reviewing | Comments without context or follow-up | Require summary; follow up on requested changes |
| Scope creep | "While you're here, can you also..." | New work gets its own PR and ticket |
| Approval hoarding | Requiring 4+ approvals "for safety" | 1-2 approvals with clear ownership |
| Concern | What AI Catches Well | What Needs Human Judgment |
|---|---|---|
| Bugs | Null derefs, off-by-ones, resource leaks | Business logic correctness |
| Style | Inconsistencies, formatting | Naming quality, abstraction appropriateness |
| Security | Injection, XSS, auth issues | Threat model fit |
| Coverage | Missing test coverage | Test quality |
| Architecture | Duplication across files | Whether the code solves the right problem |
AI-generated code requires more review rigor: 1.75x more logic errors than human code, 45% contain security vulnerabilities. Authors must understand and explain every line.
| Signal | Focus Area |
|---|---|
| New contributor's PR | Correctness + mentoring |
| Touches auth/payments/security | Security-first review |
| Large refactoring | Design + test coverage |
| Bug fix | Correctness + regression tests |
| AI-generated code | Everything + extra scrutiny |
| Condition | Decision |
|---|---|
| All blockers addressed, tests pass, design is sound | Approve |
| Minor nitpicks only | Approve with comments |
| Design concerns but implementation is correct | Request changes |
| Missing tests for new behavior | Request changes |
| Too large to review effectively | Request split |
