AI Code Reviewer Skill

AI Code Reviewer Skill

Purpose

Leverages external AI models (OpenAI Codex CLI and Google Gemini CLI) for deep code analysis beyond Claude's built-in capabilities. Provides multi-perspective code reviews with result aggregation and consensus scoring.

Prerequisites

At least one of the following must be installed and authenticated:

• Codex CLI: Run codex auth to authenticate
• Gemini CLI: Run gemini auth login to authenticate

When to Use

• Deep security analysis requiring external AI perspective
• Performance optimization requests needing specialized analysis
• Multi-model code review for high-confidence findings
• Large codebase analysis with result caching
• Secret and credential detection in code

Available MCP Tools

analyze_code_with_codex

Uses OpenAI Codex for comprehensive code analysis.

• Best for: General code review, bug detection, logical errors
• Input: Code snippet with optional context (project type, language, focus areas)
• Output: Structured findings with severity levels and suggestions

analyze_code_with_gemini

Uses Google Gemini for code analysis.

• Best for: Performance analysis, architectural review, style consistency
• Input: Code snippet with optional context
• Output: Structured findings with code examples

analyze_code_combined

Aggregates results from both Codex and Gemini with deduplication.

• Best for: High-stakes reviews requiring consensus
• Features:
  • Parallel execution for speed
  • Result deduplication with similarity threshold
  • Confidence scoring based on agreement
• Output: Merged findings with source attribution

scan_secrets

Detects hardcoded secrets, API keys, credentials, and sensitive data.

• Best for: Pre-commit security checks
• Patterns: AWS, GCP, Azure, GitHub, database credentials, private keys
• Excludes: Test files, mock files by default
• Output: Secret findings with severity and remediation suggestions

get_analysis_status

Retrieves status of async analysis operations.

• Input: Analysis ID from previous tool call
• Output: Status (pending/​in_progress/​completed/​failed), result or error

Workflow

Step 1: Determine Analysis Type

Ask user for analysis preference:

Question: "What type of AI analysis do you need?"

Options:

1. Quick Review - Single model, faster (Codex OR Gemini)
2. Deep Review - Combined models with consensus scoring
3. Security Scan - Secret detection only
4. Performance Focus - Optimization-focused review
5. Full Audit - Combined + secret scan

Step 2: Model Selection (if Quick Review)

Question: "Which AI model should be used?"

Options:

1. Codex (OpenAI) - Better for bug detection, logical errors
2. Gemini (Google) - Better for architectural patterns, style

Step 3: Set Context (Optional)

Question: "What's the project context?"

Options:

1. Auto-detect - Infer from code
2. Web App (React/​Vue) - Frontend focus
3. API (Node/​Express) - Backend focus
4. MCP Server - Protocol focus
5. CLI Tool - User tool focus
6. Library - Reusability focus

Step 4: Execute Analysis

Call the appropriate MCP tool based on selections.

Step 5: Present Results

Format findings in structured markdown with:

• Overall assessment
• Summary statistics
• Grouped findings by severity
• Actionable recommendations

Response Template

## AI Code Review Results

**Analysis ID**: [id]
**Models Used**: [codex/​gemini/​combined]
**Cache Status**: [hit/​miss]
**Duration**: [Xms]

### Overall Assessment

[AI-generated overall assessment of code quality]

### Summary

| Severity | Count |
|----------|-------|
| Critical | X |
| High | X |
| Medium | X |
| Low | X |
| **Total** | **X** |

### Findings

#### Critical Issues

1. **[Title]** (Line X)
   - **Description**: [...]
   - **Suggestion**: [...]
   - **Code**: `[snippet]`

#### High Priority

[...]

### Recommendations

1. [Prioritized action item 1]
2. [Prioritized action item 2]
3. [...]

---
*Analysis by [model(s)] | Confidence: [X]% | Duration: [X]ms*

Integration Notes

• Works alongside code-reviewer for comprehensive analysis
• Complements security-scanner with external AI perspective
• Results are cached (1 hour TTL) for repeated queries
• Secret scanning runs locally, no external API calls
• Triggered by /​cr express command

Error Handling

• CLI Not Found: Gracefully reports missing CLI, suggests installation
• Authentication Failed: Guides user through auth process
• Timeout: Returns partial results with warning
• Rate Limited: Queues requests with exponential backoff

Performance Notes

• Combined analysis runs in parallel by default
• Cache reduces repeated analysis costs
• Large files are truncated with warning
• SQLite storage for persistent cache