---

name: software-code-review description: Patterns, checklists, and templates for systematic code review with a focus on correctness, security, readability, performance, and maintainability.

Code Reviewing Skill — Quick Reference

This skill provides operational checklists and prompts for structured code review across languages and stacks. Use it when the primary task is reviewing existing code rather than designing new systems.

---

Quick Reference

---

Specialized: .NET/EF Core Crypto Integration

Skip unless reviewing C#/.NET crypto/fintech services using Entity Framework Core.

For C#/.NET crypto/fintech services using Entity Framework Core, see:

• references/dotnet-efcore-crypto-rules.md — Complete review rules (correctness, security, async, EF Core, tests, MRs)

Key rules summary:

• Review only new/modified code in the MR
• Use decimal for financial values, UTC for dates
• Follow CC-SEC-03 (no secrets in code) and CC-OBS-02 (no sensitive data in logs)
• Async for I/O, pass CancellationToken, avoid .Result/.Wait() (see CC-ERR-04, CC-FLOW-03)
• EF Core: AsNoTracking for reads, avoid N+1, no dynamic SQL
• Result<T> pattern for explicit success/fail

---

When to Use This Skill

Invoke this skill when the user asks to:

• Review a pull request or diff for issues
• Audit code for security vulnerabilities or injection risks
• Improve readability, structure, and maintainability
• Suggest targeted refactors without changing behavior
• Validate tests and edge-case coverage

Decision Tree: Selecting Review Mode

CopyCode review task: [What to Focus On?]
    ├─ Security-critical changes?
    │   ├─ Auth/access control → Security Review (OWASP, auth patterns)
    │   ├─ User input handling → Input validation, XSS, SQL injection
    │   └─ Smart contracts → Blockchain Review (reentrancy, access control)
    │
    ├─ Performance concerns?
    │   ├─ Database queries → Check for N+1, missing indexes
    │   ├─ Loops/algorithms → Complexity analysis, caching
    │   └─ API response times → Profiling, lazy loading
    │
    ├─ Correctness issues?
    │   ├─ Business logic → Edge cases, error handling, tests
    │   ├─ Data transformations → Boundary conditions, null checks
    │   └─ Integration points → Retry logic, timeouts, fallbacks
    │
    ├─ Maintainability problems?
    │   ├─ Complex code → Naming, function length, duplication
    │   ├─ Hard to understand → Comments, abstractions, clarity
    │   └─ Technical debt → Refactoring suggestions
    │
    ├─ Test coverage gaps?
    │   ├─ New features → Happy path + error cases
    │   ├─ Refactors → Regression tests
    │   └─ Bug fixes → Reproduction tests
    │
    └─ Stack-specific review?
        ├─ Frontend → [frontend-review.md](assets/web-frontend/frontend-review.md)
        ├─ Backend → [api-review.md](assets/backend-api/api-review.md)
        ├─ Mobile → [mobile-review.md](assets/mobile/mobile-review.md)
        ├─ Infrastructure → [infrastructure-review.md](assets/infrastructure/infrastructure-review.md)
        └─ Blockchain → [crypto-review.md](assets/blockchain/crypto-review.md)

Multi-Mode Reviews:

For complex PRs, apply multiple review modes sequentially:

1. Security first (P0/P1 issues)
2. Correctness (logic, edge cases)
3. Performance (if applicable)
4. Maintainability (P2/P3 suggestions)

---

Async Review Workflows (Dec 2025)

Timezone-Friendly Reviews

Non-Blocking Reviews

CopyPR Submitted -> Auto-checks (CI) -> Async Review -> Merge
       |              |               |
  Author continues   If green,    Reviewer comments
  on other work      queue for    when available
                     review

Anti-patterns:

• Synchronous review meetings for routine PRs
• Blocking on reviewer availability for non-critical changes
• Single reviewer bottleneck

Review Prioritization Matrix

---

Optional: AI/Automation Extensions

Note: AI-assisted review tools. Human review remains authoritative.

AI Review Assistants

AI assistant rules:

• AI suggestions are advisory only
• Human reviewer approves/rejects
• AI cannot bypass security review
• AI findings require manual verification

AI Review Checklist

• AI suggestions validated against codebase patterns
• AI-flagged issues manually confirmed
• False positives documented for tool improvement
• Human reviewer explicitly approved

---

Simplicity and Complexity Control

• Prefer existing, battle-tested libraries over bespoke implementations when behavior is identical.
• Flag avoidable complexity early: remove dead/commented-out code, collapse duplication, and extract single-responsibility helpers.
• Call out premature optimization; favor clarity and measured, evidence-based tuning.
• Encourage incremental refactors alongside reviews to keep modules small, predictable, and aligned to standards.

---

Operational Playbooks

Shared Foundation

• ../software-clean-code-standard/references/clean-code-standard.md - Canonical clean code rules (CC-*) for citation in reviews
• Legacy playbook: ../software-clean-code-standard/references/code-quality-operational-playbook.md - RULE-01–RULE-13, refactoring decision trees, and design patterns

Code Review Specific

• references/operational-playbook.md — Review scope rules, severity ratings (P0-P3), checklists, modes, and PR workflow patterns

Navigation

Resources

• references/operational-playbook.md
• references/review-checklist-comprehensive.md
• references/implementing-effective-code-reviews-checklist.md
• references/looks-good-to-me-checklist.md
• references/automation-tools.md
• references/dotnet-efcore-crypto-rules.md
• references/psychological-safety-guide.md

Templates

• assets/core/pull-request-description-template.md
• assets/core/review-checklist-judgment.md
• assets/core/review-comment-guidelines.md
• assets/backend-api/api-review.md
• assets/web-frontend/frontend-review.md
• assets/mobile/mobile-review.md
• assets/infrastructure/infrastructure-review.md
• assets/blockchain/crypto-review.md
• assets/data-ml/data-pipeline-review.md
• assets/data-ml/experiment-tracking-review.md
• assets/data-ml/ml-model-review.md
• assets/data-ml/ml-deployment-review.md

Data

• data/sources.json — Curated external references
• Shared checklists: ../software-clean-code-standard/assets/checklists/secure-code-review-checklist.md, ../software-clean-code-standard/assets/checklists/backend-api-review-checklist.md

---

Trend Awareness Protocol

IMPORTANT: When users ask recommendation questions about code review tools, practices, or automation, you MUST use WebSearch to check current trends before answering.

Trigger Conditions

• "What's the best code review tool?"
• "What should I use for [automated code review/PR automation]?"
• "What's the latest in code review practices?"
• "Current best practices for [code review/PR workflow]?"
• "Is [GitHub Copilot PR/CodeRabbit] still relevant in 2026?"
• "[CodeRabbit] vs [Graphite] vs [other]?"
• "Best AI code review assistant?"

Required Searches

1. Search: "code review best practices 2026"
2. Search: "[specific tool] vs alternatives 2026"
3. Search: "AI code review tools January 2026"
4. Search: "PR automation trends 2026"

What to Report

After searching, provide:

• Current landscape: What code review tools/practices are popular NOW
• Emerging trends: New AI assistants, PR tools, or review patterns gaining traction
• Deprecated/declining: Tools/approaches losing relevance or support
• Recommendation: Based on fresh data, not just static knowledge

Example Topics (verify with fresh search)

• AI code review (GitHub Copilot PR, CodeRabbit, Cursor)
• PR automation (Graphite, Stacked PRs, merge queues)
• Code review platforms (GitHub, GitLab, Bitbucket)
• Review bots and automation
• Async review practices for distributed teams
• Review metrics and analytics tools