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Code review

Skill itallstartedwithaidea/agent-skills/skills/software-dev/code-review

Code Review enforces a structured pre-merge quality gate with a checklist-driven evaluation, severity-classified findings, and mandatory resolution tracking.From its SKILL.md

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npx -y skills add itallstartedwithaidea/agent-skills --skill code-review

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SKILL.md

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Code Review

Part of Agent Skills™ by googleadsagent.ai™

Description

Code Review enforces a structured pre-merge quality gate with a checklist-driven evaluation, severity-classified findings, and mandatory resolution tracking. The agent reviews every diff against a configurable set of quality dimensions before approving changes, ensuring consistent standards regardless of reviewer fatigue or time pressure.

Unlike ad-hoc review comments, this skill produces a standardized review document with findings categorized by severity: Critical (must fix before merge), High (should fix before merge), Medium (fix in follow-up), and Low (optional improvement). Each finding includes the file, line range, category, description, and a concrete suggested fix. The review document becomes part of the permanent record.

The pre-review checklist catches common oversights before deep analysis begins: missing tests, uncommitted files, linter errors, type errors, and documentation gaps. Only after the checklist passes does the agent proceed to semantic review of logic, architecture, security, and performance.

Use When

  • A pull request or diff is ready for review
  • The user asks for feedback on code changes
  • Before merging any branch into main
  • After a subagent completes a task (Stage 2 review)
  • Code has been refactored and needs validation
  • A new contributor's code needs onboarding-level review

How It Works

graph TD
    A[Receive Diff] --> B[Pre-Review Checklist]
    B --> C{Checklist Passes?}
    C -->|No| D[Return with Blockers]
    C -->|Yes| E[Semantic Analysis]
    E --> F[Classify Findings by Severity]
    F --> G[Generate Review Document]
    G --> H{Critical Findings?}
    H -->|Yes| I[Request Changes]
    H -->|No| J{High Findings?}
    J -->|Yes| K[Approve with Reservations]
    J -->|No| L[Approve]

The workflow gates progression: the checklist catches mechanical issues instantly, while semantic analysis evaluates design, correctness, and maintainability. The severity classification ensures critical issues block the merge while minor improvements do not.

Implementation

pre_review_checklist:
  - name: "Tests exist for changed code"
    command: "check_test_coverage_delta"
    severity: "critical"
  - name: "No linter errors introduced"
    command: "run_linter --diff-only"
    severity: "critical"
  - name: "Type checking passes"
    command: "run_typecheck"
    severity: "critical"
  - name: "No secrets in diff"
    command: "scan_secrets --diff"
    severity: "critical"
  - name: "Documentation updated"
    command: "check_doc_staleness"
    severity: "medium"

severity_levels:
  critical:
    label: "🔴 Critical"
    action: "Must fix before merge"
    examples: ["Security vulnerability", "Data loss risk", "Broken tests"]
  high:
    label: "🟠 High"
    action: "Should fix before merge"
    examples: ["Missing error handling", "Performance regression", "API contract violation"]
  medium:
    label: "🟡 Medium"
    action: "Fix in follow-up PR"
    examples: ["Code duplication", "Unclear naming", "Missing edge case test"]
  low:
    label: "🔵 Low"
    action: "Optional improvement"
    examples: ["Style preference", "Minor refactor opportunity", "Comment improvement"]

review_dimensions:
  - correctness: "Does the code do what it claims?"
  - security: "Are inputs validated? Are secrets protected?"
  - performance: "Are there N+1 queries, unnecessary re-renders, or blocking calls?"
  - maintainability: "Can another developer understand this in 6 months?"
  - testing: "Are edge cases covered? Are tests deterministic?"
  - architecture: "Does this follow established patterns? Is coupling appropriate?"

Best Practices

  • Run the pre-review checklist before spending time on semantic review
  • Limit findings to actionable items—avoid stylistic nitpicks unless egregious
  • Always provide a suggested fix alongside each finding
  • Separate critical blockers from nice-to-have improvements
  • Review the test changes as carefully as the production code
  • Acknowledge good patterns and design decisions, not just defects

Platform Compatibility

PlatformSupportNotes
CursorFullReads diffs via Shell tool
VS CodeFullGit diff integration
WindsurfFullPR review workflow
Claude CodeFullgit diff and gh pr access
ClineFullDiff-based review
aiderPartialLimited to file-level review

Related Skills

Keywords

code-review quality-gate pre-merge-checklist severity-classification pull-request-review security-review performance-review review-document


© 2026 googleadsagent.ai™ | Agent Skills™ | MIT License

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most code review skills give in ~1.2k tokens

Counted across 668 of the 814 authors here whose files we hold, read 2026-09-06

  • Provide technical reasoning when pushing backin 84 of 668, across 70 files
  • Fix critical issues immediatelyin 77 of 668, across 60 files
  • Dispatch a code reviewer subagentin 76 of 668, across 59 files
  • Fix important issues before proceedingin 73 of 668, across 56 files
  • Ask for clarification on unclear itemsin 68 of 668, across 56 files
  • Verify feedback against codebase before implementationin 66 of 668, across 55 files
  • Implement fixes one at a timein 64 of 668, across 53 files
  • Test each fix individuallyin 62 of 668, across 51 files
  • Restate technical requirements in own wordsin 57 of 668, across 46 files
  • Reply to inline comments in the specific threadin 51 of 668, across 40 files
  • Note minor issues for laterin 49 of 668, across 34 files
  • Group findings by severityin 48 of 668, across 47 files

Said here and by no other author read

  • Run pre-review checklist before semantic analysis
  • Review test changes as carefully as production code
  • Limit findings to actionable items

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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