ai-code-review

v4.1.0 suspicious
4.0
Medium Risk

AI code review tool that works with any language model provider. It detects issues in GitHub pull requests or local changes—instantly, reliably, and without vendor lock-in.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse, particularly in its handling of credentials and network calls, which require closer scrutiny.

  • Network risk due to external calls
  • Potential credential mishandling
Per-check LLM notes
  • Network: The package makes network calls which could be for legitimate purposes like fetching updates or resources, but requires further investigation to ensure no unauthorized data exfiltration is occurring.
  • Shell: No shell execution patterns detected, suggesting low risk of direct command execution from the package.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code attempts to retrieve a GitHub token from environment variables, which could be a legitimate practice but also poses a risk if not handled securely.
  • Metadata: The maintainer has only one package, suggesting they may be new or less active, but no other red flags were identified.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Nayjest/Gito#readme
  • Detailed PyPI description (17587 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 111 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 11 unique contributor(s) across 100 commits in Nayjest/Gito
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ct.zip" try: with requests.get(url, headers=headers, stream=True) as r: r.raise
  • ATE-TOKEN": token} resp = requests.post(url, headers=headers, json={"body": body}, timeout=30) i
  • ge}" ) resp = requests.get(url, headers=headers, timeout=30) if resp.status_cod
  • ATE-TOKEN": token} resp = requests.put(url, headers=headers, json={"body": new_body}, timeout=30)
  • " try: response = requests.post( "https://api.linear.app/graphql", h
  • = {"body": text} resp = requests.post(api_url, headers=headers, json=data) if 200 <= resp.stat
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • : return token_or_none or os.getenv("GITHUB_TOKEN", None) or os.getenv("GH_TOKEN", None) def post_gh_commen
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: vitaliy.in

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Nayjest/Gito appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Vitalii Stepanenko" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-code-review 
Create a web-based application using Flask or Django that integrates the 'ai-code-review' package to provide real-time code analysis and feedback for developers. This application will allow users to either upload their code snippets or connect to their GitHub repositories to analyze pull requests. The application should have the following core functionalities:

1. User Authentication: Implement user registration and login functionality using Flask-Security or Django's built-in authentication system.
2. Code Snippet Upload: Allow users to upload code snippets directly from their local machine or copy-paste them into the application. The application should support multiple programming languages.
3. GitHub Integration: Users should be able to authorize the application to access their GitHub accounts and select specific repositories or pull requests for analysis.
4. Real-Time Analysis: Upon uploading a snippet or selecting a GitHub pull request, the application should use the 'ai-code-review' package to analyze the code for potential issues, best practices, and coding standards violations.
5. Feedback Display: Present the analysis results in a user-friendly manner, highlighting the detected issues and suggesting improvements. The feedback should include severity levels for each issue.
6. Issue Tracking: Allow users to mark specific issues as resolved within the application and track the progress of their code improvements.
7. Custom Rulesets: Provide an option for advanced users to configure custom rulesets for the 'ai-code-review' package, allowing for tailored code reviews based on company-specific guidelines or personal preferences.
8. Documentation and Help: Include comprehensive documentation and a help section explaining how to use the application effectively, along with examples and best practices.

The 'ai-code-review' package is utilized throughout the application to perform the actual code analysis. It will be integrated into the backend logic where it receives code inputs, processes them, and returns the analysis results. The application will need to handle various types of input data formats and ensure compatibility across different programming languages supported by 'ai-code-review'. Additionally, the application should demonstrate flexibility in integrating with different language models providers to avoid vendor lock-in.