🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to its ability to execute shell commands and the lack of detailed maintainer information.

Shell execution capability present
Sparse maintainer information

Per-check LLM notes

Network: No network calls were detected, which is normal if the package does not require internet access.
Shell: The presence of shell execution suggests the package may execute external commands, which could be legitimate if it's designed to interact with system tools. However, this also increases the risk of potential misuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository has low activity and the maintainer's information is sparse, indicating potential risks.

🔬 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)

roceed normally try: r = subprocess.run( ["booster", "smart-read", rel], capture_out
proceed try: r = subprocess.run( ["booster", "search", pattern], cap
ent.parent try: result = subprocess.run( ["booster", "route", safe_message], capture

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: b2bsphere.com>

✓ 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 agent-booster

Create a Python-based code analysis tool named 'CodeNest' that leverages the 'agent-booster' package to enhance its performance and efficiency. This tool will analyze Python source code files and provide insights such as function usage, variable scope, and potential bugs. It should also offer suggestions for improving code quality and efficiency.

Step 1: Setup the Project Environment
- Initialize a new Python project.
- Install necessary packages including 'agent-booster', 'asttokens', and 'numpy'.

Step 2: Design the Core Functionality
- Develop a parser using 'agent-booster' to process Python code into an Abstract Syntax Tree (AST).
- Implement a vector context router to optimize the parsing process and reduce token costs by 5-15 times compared to traditional methods.

Step 3: Analyze Code
- Create functions to traverse the AST and identify key elements like functions, variables, and imports.
- Analyze these elements to detect common issues such as unused variables, overly complex functions, and potential bugs.

Step 4: Provide Recommendations
- Based on the analysis, generate recommendations for refactoring and improving code quality.
- Suggest best practices and optimizations based on industry standards and coding guidelines.

Suggested Features:
- Interactive command-line interface for user interaction.
- Option to save analysis reports in various formats (JSON, HTML).
- Integration with popular version control systems (Git) to analyze changes between commits.
- Support for multiple Python file types (e.g., .py, .ipynb).

How 'agent-booster' is Utilized:
- Use 'agent-booster' to efficiently parse large Python codebases by routing the AST through a vector context, significantly reducing the computational overhead and speeding up the analysis process. This allows 'CodeNest' to handle extensive projects without significant performance degradation.