scripts-ai-review

#!/usr/bin/env python3 """

title: "Ai Review" component_type: script version: "1.0.0" audience: contributor status: stable summary: "CODITECT AI-Powered Code Review Script ======================================" keywords: ['analysis', 'api', 'git', 'review', 'security'] tokens: ~500 created: 2025-12-22 updated: 2025-12-22 script_name: "ai-review.py" language: python executable: true usage: "python3 scripts/ai-review.py [options]" python_version: "3.10+" dependencies: [] modifies_files: false network_access: false requires_auth: false

CODITECT AI-Powered Code Review Script

STATUS: STUB - Not yet implemented VERSION: 0.1.0 (placeholder) AUTHOR: CODITECT Core Team

DESCRIPTION: AI-enhanced code review tool that analyzes pull requests using LLM models (GPT-4, Claude, etc.) combined with static analysis results to provide intelligent, context-aware code review comments.

PURPOSE: - Analyze PR diffs for code quality issues - Integrate with static analysis tools (CodeQL, Semgrep, ESLint) - Generate actionable review comments in JSON format - Post comments to GitHub PRs via GitHub Actions

EXPECTED INPUTS: --pr-number : GitHub PR number to review --model : LLM model to use (gpt-4o, claude-3, etc.) --repo : Repository in owner/repo format (default: from env) --static-analysis-results : Comma-separated SARIF files from static analyzers --output : Output file for review comments (default: review-comments.json) --severity : Minimum severity to report (info, warning, error) --max-comments : Maximum number of comments to generate (default: 20)

EXPECTED OUTPUTS: - review-comments.json: Array of review comment objects with: { "path": "src/file.py", "line": 42, "body": "Consider using...", "severity": "warning", "category": "performance|security|style|logic" }

DEPENDENCIES: - openai or anthropic (for LLM API) - PyGithub (for GitHub API interaction) - sarif-tools (for parsing SARIF files) - requests (for API calls)

IMPLEMENTATION REQUIREMENTS: 1. GitHub API integration to fetch PR diff 2. SARIF parser to ingest static analysis results 3. LLM prompt engineering for code review context 4. Rate limiting and token management 5. Comment deduplication and prioritization 6. Support for multiple LLM providers 7. Caching to avoid re-reviewing unchanged files

ENVIRONMENT VARIABLES: OPENAI_API_KEY : OpenAI API key (for GPT models) ANTHROPIC_API_KEY : Anthropic API key (for Claude models) GITHUB_TOKEN : GitHub token for PR access GITHUB_REPOSITORY : Repository in owner/repo format

USAGE EXAMPLES: # Basic PR review python scripts/ai-review.py --pr-number 123 --model gpt-4o

# With static analysis integration
python scripts/ai-review.py \\
    --pr-number 123 \\
    --model gpt-4o \\
    --static-analysis-results codeql.sarif,semgrep.sarif

# Claude model with custom output
python scripts/ai-review.py \\
    --pr-number 123 \\
    --model claude-3-opus \\
    --output my-review.json \\
    --max-comments 10

RELATED COMMANDS: - /ai-review : Slash command that invokes this script - /analyze : Code analysis mode

SEE ALSO: - commands/ai-review.md - docs/02-user-guides/CODE-REVIEW-GUIDE.md """

import argparse import json import sys from pathlib import Path

def main(): parser = argparse.ArgumentParser( description='AI-Powered Code Review for GitHub PRs', formatter_class=argparse.RawDescriptionHelpFormatter, epilog=''' Examples: %(prog)s --pr-number 123 --model gpt-4o %(prog)s --pr-number 123 --model claude-3-opus --static-analysis-results codeql.sarif

Status: STUB - Implementation required ''' )

parser.add_argument('--pr-number', type=int, required=True,
                    help='GitHub PR number to review')
parser.add_argument('--model', default='gpt-4o',
                    choices=['gpt-4o', 'gpt-4-turbo', 'claude-3-opus', 'claude-3-sonnet'],
                    help='LLM model for review (default: gpt-4o)')
parser.add_argument('--repo', default=None,
                    help='Repository (owner/repo), default from GITHUB_REPOSITORY')
parser.add_argument('--static-analysis-results', default=None,
                    help='Comma-separated SARIF files')
parser.add_argument('--output', default='review-comments.json',
                    help='Output JSON file (default: review-comments.json)')
parser.add_argument('--severity', default='warning',
                    choices=['info', 'warning', 'error'],
                    help='Minimum severity to report')
parser.add_argument('--max-comments', type=int, default=20,
                    help='Maximum comments to generate')
parser.add_argument('--dry-run', action='store_true',
                    help='Show what would be done without making API calls')

args = parser.parse_args()

print("=" * 70)
print("CODITECT AI-REVIEW - STUB IMPLEMENTATION")
print("=" * 70)
print(f"\nThis script is a placeholder stub.")
print(f"Full implementation is required.\n")
print(f"Configuration:")
print(f"  PR Number: {args.pr_number}")
print(f"  Model: {args.model}")
print(f"  Repository: {args.repo or 'from GITHUB_REPOSITORY env'}")
print(f"  Static Analysis: {args.static_analysis_results or 'none'}")
print(f"  Output: {args.output}")
print(f"  Severity: {args.severity}")
print(f"  Max Comments: {args.max_comments}")
print()

# Create stub output
stub_output = {
    "status": "stub",
    "message": "AI review not yet implemented",
    "pr_number": args.pr_number,
    "model": args.model,
    "comments": []
}

with open(args.output, 'w') as f:
    json.dump(stub_output, f, indent=2)

print(f"Stub output written to: {args.output}")
print("\nTo implement this script, see the docstring requirements above.")

return 0

if name == 'main': sys.exit(main())

#!/usr/bin/env python3 """​

CODITECT AI-Powered Code Review Script

#!/usr/bin/env python3 """