aicodetools

v2.0.61 suspicious
4.0
Medium Risk

Simple, lightweight AI code tools with Docker-only support - no complex dependencies

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse due to its interaction with Docker, which can be risky if not handled securely. However, there's no clear evidence of malicious intent.

  • Shell risk due to Docker command execution
  • Unverified author with a single package
Per-check LLM notes
  • Network: The use of connection pooling and keep-alive is generally benign and may improve performance.
  • Shell: Executing Docker commands suggests the package is intended to interact with Docker, but it could pose risks if not properly secured or vetted.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, and the repository is not found, which raises some suspicion but does not conclusively indicate malice.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/balajidinesh/aicodetools
  • Detailed PyPI description (13979 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

  • 77 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • dling self.session = requests.Session() # Set connection pooling and keep-alive for bet
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • try: result = subprocess.run( ['docker', 'images', '-q', image_name],
  • ..") result = subprocess.run( build_cmd, capture_
  • ry: result = subprocess.run(['docker', 'info'], capture_output=True, text=True, timeout=
  • check_result = subprocess.run(['docker', 'images', '-q', self.docker_image],
  • pull_result = subprocess.run(['docker', 'pull', self.docker_image],
  • isting container subprocess.run(['docker', 'stop', self.container_name], capture_output=True
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "balajidinesh" 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 aicodetools 
Create a simple yet powerful code analysis tool using the 'aicodetools' package. This tool will help developers analyze their Python code for common issues like complexity, potential bugs, and adherence to coding standards. Here’s how you can approach building this application:

1. **Setup Environment**: Start by setting up your development environment using Docker as per the 'aicodetools' package requirements. Ensure that all necessary Docker images are pulled and configured.

2. **Project Structure**: Define the project structure. Create directories for source code, tests, and documentation.

3. **Core Features**:
   - **Code Complexity Analysis**: Implement a feature that calculates the cyclomatic complexity of functions and methods in Python files. Use 'aicodetools' to facilitate this process without needing to install additional complexity calculation libraries.
   - **Bug Detection**: Integrate basic bug detection capabilities, such as identifying unused variables, unreachable code, and other common pitfalls.
   - **Coding Standards Check**: Add a feature that checks code against popular Python style guides like PEP8. Use 'aicodetools' to streamline this check process.

4. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with your tool. Users should be able to input file paths or directories, and receive feedback on code quality and potential issues.

5. **Testing**: Write comprehensive tests to ensure each feature works as expected. Use 'aicodetools' to manage these tests within the Docker environment.

6. **Documentation**: Provide clear documentation on how to use your tool, including setup instructions, usage examples, and how to interpret the results.

7. **Deployment**: Package your application so it can be easily deployed using Docker. Ensure that users can run your tool without needing to install any dependencies other than Docker.

This project aims to demonstrate the power of 'aicodetools' in simplifying the development of AI-driven code analysis tools, making it easier for developers to maintain high-quality code.