arcmesh

v0.1.2 suspicious
4.0
Medium Risk

One command. Your codebase, AI-ready.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some suspicious elements, particularly regarding shell execution and metadata issues, which warrant caution.

  • Shell risk detected requiring further investigation
  • Metadata risk due to incomplete author information and potentially inactive maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution patterns detected may be for benign purposes like retrieving system information, but require further investigation to ensure they do not serve malicious intent.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags such as an author with a missing or short name and a new or inactive maintainer account.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1268 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 18 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 30 commits in arcmesh-labs/arcmesh-cli
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • s). try: result = subprocess.run( ["wsl.exe", "-l", "-q"], capture_ou
  • tr: try: result = subprocess.run( ["which", command], capture_output=
  • ]": try: result = subprocess.run( ["cmd.exe", "/c", "echo", "%USERNAME%"],
✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 arcmesh 
Your task is to develop a Python-based mini-application named 'AI-CodePrep' which leverages the 'arcmesh' package to prepare any given Python codebase for integration with AI services. The application should allow users to upload a directory containing their Python code, and then process it to make it AI-friendly. Here’s a step-by-step guide on how to proceed:

1. **Project Setup**: Start by setting up your Python environment. Ensure you have the latest version of 'arcmesh' installed.
2. **User Interface**: Design a simple command-line interface (CLI) where users can interact with the application. Users should be able to specify the path to their Python codebase as input.
3. **Code Analysis**: Implement functionality within 'AI-CodePrep' that analyzes the uploaded codebase. This includes identifying Python files, understanding dependencies, and detecting potential issues that could hinder AI integration.
4. **AI Readiness Transformation**: Based on the analysis, apply transformations to the codebase to enhance its compatibility with AI services. This could include adding necessary imports, modifying code structure, and suggesting changes for better performance with AI models.
5. **Output Generation**: Once the transformations are complete, generate a report detailing the changes made and provide the updated codebase ready for AI integration.
6. **Optional Features**:
   - Integration with popular AI services like TensorFlow or PyTorch.
   - Suggestions for refactoring code to improve efficiency.
   - A feature that automatically tests the transformed code to ensure no functional regressions.
7. **Documentation**: Create comprehensive documentation explaining how to use 'AI-CodePrep', including examples and best practices.
8. **Testing & Validation**: Thoroughly test the application using various Python projects to validate its effectiveness in preparing codebases for AI integration.

Throughout the development process, focus on utilizing 'arcmesh' effectively to streamline the preparation of the codebase, ensuring the application is user-friendly and efficient.

💬 Discussion Feed

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