agentbible

v1.0.1 suspicious
5.0
Medium Risk

Language-agnostic correctness checker for scientific numerical code

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation through dynamic imports, which raises suspicion. While there are no direct indicators of malicious activity such as network calls or credential harvesting, the low engagement in the repository and the maintainer's status warrant caution.

  • Obfuscation risk due to dynamic imports
  • Low engagement and new/inactive maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal and does not indicate risk.
  • Shell: Git commands suggest the package is performing version control operations, which is common for development but could indicate CI/CD automation that needs further scrutiny.
  • Obfuscation: The use of dynamic imports with error handling suggests an attempt to obfuscate the code and potentially load modules dynamically, which is suspicious.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The repository's lack of engagement and the maintainer's new or inactive status raise concerns about potential malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • try: module = __import__(name) except ImportError: continue ve
  • e"): try: __import__(module_name) except ImportError: continue re
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • """ try: result = subprocess.run( ["gh", *args], capture_output=True,
  • orking directory result = subprocess.run( ["git", "status", "--porcelain"], capture_o
  • # Commit version bump subprocess.run( ["git", "add", "-A"], capture_outpu
  • utput=True, ) subprocess.run( ["git", "commit", "-m", f"chore: bump version t
  • ag_version}...") result = subprocess.run( ["git", "tag", "-a", tag_version, "-m", f"Release {
  • remote...") result = subprocess.run( ["git", "push", "origin", "main"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.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 agentbible 
Create a Python-based mini-application called 'NumCheck' that leverages the 'agentbible' package to ensure the accuracy of numerical computations in scientific research. This tool will serve as a critical aid for researchers and scientists who need to validate their computational models against known solutions. Here’s a detailed plan on how to develop NumCheck:

1. **Project Setup**: Begin by setting up your Python environment. Ensure you have Python 3.8 or higher installed. Use pip to install 'agentbible' and any other necessary packages such as numpy for numerical operations.
2. **Core Functionality**: Develop the core functionality of NumCheck which involves defining functions for common numerical computations like matrix multiplication, differential equations solving, and Fourier transforms. Each function will take inputs and expected outputs as parameters.
3. **Integration with AgentBible**: Utilize 'agentbible' to verify the correctness of these functions. Implement checks that compare the output of each function against a known solution or theoretical expectation. Use 'agentbible' to automatically flag discrepancies and provide detailed reports on where and why the function failed.
4. **User Interface**: Design a simple command-line interface (CLI) that allows users to input their own numerical problems and check them against known solutions. Users should be able to upload their custom functions or use pre-defined ones provided by NumCheck.
5. **Reporting Mechanism**: Implement a feature within NumCheck that generates comprehensive reports whenever a discrepancy is found. These reports should include error margins, visual plots comparing actual vs expected results, and suggestions for debugging.
6. **Testing and Validation**: Rigorously test all functionalities using a set of predefined test cases. Validate the accuracy of NumCheck itself by comparing its findings with those from established numerical computation tools.
7. **Documentation and Deployment**: Write clear documentation explaining how to use NumCheck, including setup instructions and examples. Consider deploying NumCheck as a web service so it can be accessed online by researchers worldwide.

By following these steps, you'll create a powerful yet user-friendly tool that enhances the reliability of numerical computations in scientific research.