ai-cr

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 exhibits moderate risks related to network and credential handling, raising concerns about potential misuse or vulnerability to attacks.

  • moderate network risk
  • potential credential exposure
Per-check LLM notes
  • Network: The package performs network requests which could be legitimate depending on its functionality, but it may also indicate potential data exfiltration or unauthorized communication.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected in the code snippet.
  • Credentials: The code retrieves environment variables which may be used for authenticating API requests, indicating potential risk of credential exposure if not handled properly.
  • Metadata: The maintainer has only one package, suggesting a new or less active account, but no other red flags are present.

📦 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-cr 
Create a fully-functional mini-application named 'CodeGuard' using the Python package 'ai-cr'. This application will serve as a personal code reviewer, allowing users to input their code snippets or provide a link to a GitHub pull request. The application will then analyze the provided code using the 'ai-cr' package and output potential issues, suggestions for improvement, and compliance checks against coding standards.

Step-by-Step Guide:
1. Setup the Project Environment: Ensure Python 3.x is installed on your system and install the 'ai-cr' package via pip.
2. Design the User Interface: Create a simple command-line interface where users can either paste their code or enter the URL of a GitHub pull request.
3. Implement Code Analysis: Utilize 'ai-cr' to analyze the code snippet or GitHub pull request. Integrate it with various language models providers like OpenAI, Anthropic, etc., ensuring there's no vendor lock-in.
4. Display Results: Present the findings from the analysis clearly, indicating any detected issues, style violations, or recommendations for better practices.
5. Optional Features: Consider adding options for users to specify preferred coding standards (e.g., PEP8 for Python), or allow them to choose which type of analysis they want (security, performance, readability).
6. Testing: Test the application with different types of code snippets and GitHub repositories to ensure accuracy and reliability.
7. Documentation: Write clear documentation explaining how to use 'CodeGuard', including installation instructions and examples of input/output.

How 'ai-cr' is Utilized: The 'ai-cr' package will be the backbone of the 'CodeGuard' application, responsible for analyzing the code and identifying potential issues. Users will benefit from real-time feedback on their code without being tied to a specific language model provider, making 'CodeGuard' adaptable and future-proof.