atlas-strata

v0.2.0 suspicious
6.0
Medium Risk

Intelligent code analysis tool for understanding any codebase. Advanced AST analysis with control flow graphs, data flow analysis, and semantic understanding.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential risks due to the use of subprocess.run for executing external commands and the lack of a discoverable repository. These factors suggest possible misuse or insufficient transparency.

  • Use of subprocess.run
  • Repository not found
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: The use of 'subprocess.run' to execute 'copilot' commands could be legitimate if the package is intended to interact with a Copilot service or tool, but requires further investigation into the purpose and permissions granted.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The repository is not found, and the maintainer has a single package, which may indicate low activity or a new account.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • 11 test file(s) detected (e.g. test_advanced_analysis.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://punitganshani.github.io/atlas-strata/
  • Detailed PyPI description (1163 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

  • 198 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: result = subprocess.run( ['copilot', '-p', full_prompt, '--allow-all
  • try: result = subprocess.run( ['copilot', '--version'], c
✓ 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 "Puneet Ghanshani" 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 atlas-strata 
Your task is to develop a Python-based code quality checker application named 'CodeInsight'. This application will utilize the 'atlas-strata' package to perform intelligent code analysis on various Python projects. The goal of CodeInsight is to provide developers with insights into their codebase, helping them improve code quality, maintainability, and performance. Here’s a detailed breakdown of what the application should accomplish:

1. **Project Setup**: Start by setting up a new Python project with all necessary dependencies, including 'atlas-strata'. Ensure that your environment is set up correctly for development.

2. **Code Analysis Engine**: Implement a code analysis engine using 'atlas-strata' that can parse Python files and generate Abstract Syntax Trees (ASTs). Use 'atlas-strata' to create control flow graphs and perform data flow analysis to understand the structure and behavior of the code.

3. **Quality Metrics**: Develop a feature that calculates several code quality metrics such as cyclomatic complexity, line count, function length, and others. These metrics should be calculated based on the AST and control flow graphs generated by 'atlas-strata'.

4. **Code Smell Detection**: Identify common code smells like long methods, large classes, duplicate code, and other anti-patterns using the semantic understanding provided by 'atlas-strata'. Provide suggestions on how to refactor the code to eliminate these smells.

5. **Dependency Analysis**: Analyze dependencies between modules and functions within the codebase. Use 'atlas-strata' to identify circular dependencies and suggest ways to decouple components.

6. **Documentation Quality Check**: Evaluate the quality of comments and docstrings in the code. Use 'atlas-strata' to ensure that every public method and class has appropriate documentation.

7. **Report Generation**: Create a user-friendly report summarizing the findings from the code analysis. Include visualizations of the AST, control flow graphs, and dependency maps where applicable. The report should also include recommendations for improving code quality.

8. **Integration and Testing**: Integrate your application with popular version control systems like Git to allow analysis of code changes over time. Write unit tests to ensure that your application works as expected.

9. **User Interface**: Develop a simple command-line interface (CLI) for interacting with CodeInsight. Additionally, consider building a basic web interface using Flask or Django for more advanced users who want to explore the results in detail.

10. **Deployment**: Package your application for deployment. Make it available via PyPI or another package manager so that other developers can easily install and use it.

By following these steps, you'll have developed a powerful tool that leverages the capabilities of 'atlas-strata' to enhance the quality and maintainability of Python codebases.

💬 Discussion Feed

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