🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks such as network calls or shell execution, but the metadata suggests potential issues with low activity and a possible change in maintainers.

Metadata risk score of 5 out of 10
Potential change in maintainer

Per-check LLM notes

Network: No network calls detected, which is typical and safe unless the package requires external resources.
Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of potential low activity and possibly a new maintainer, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ai-manifests.org>

✓ 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 adj-manifest

Create a Python-based mini-application called 'AgentJournal' that leverages the 'adj-manifest' package to manage and analyze logs from AI agents. This application will serve as a tool for developers to better understand the decision-making processes of their AI agents through detailed logging and analysis capabilities. Here’s a step-by-step guide on how to build it:

1. **Setup**: Start by installing the necessary packages including 'adj-manifest'. Ensure your environment is set up correctly for development.
2. **Logging Interface**: Develop a user-friendly interface where users can input logs manually or upload log files from AI agent deliberations. These logs should be structured according to the ADJ format supported by 'adj-manifest'.
3. **Log Analysis**: Implement functionality using 'adj-manifest' to parse and analyze these logs. This includes identifying patterns, common issues, and performance metrics related to the agent's deliberations.
4. **Visualization Tools**: Integrate visualization tools to display key insights derived from the log analysis. Use libraries like Matplotlib or Plotly for creating graphs and charts that represent agent behavior over time.
5. **Export Reports**: Allow users to export comprehensive reports based on the analyzed data. These reports should summarize findings, highlight critical areas for improvement, and suggest potential optimizations.
6. **Security Measures**: Incorporate basic security measures to protect sensitive information within the logs, ensuring compliance with privacy regulations.
7. **Testing & Documentation**: Thoroughly test all components of the application and document each feature clearly so other developers can easily use and extend 'AgentJournal'.

Suggested Features:
- Real-time log monitoring
- Customizable alerts based on predefined criteria
- Comparison tools to assess performance between different versions of the same agent
- Integration with popular AI frameworks for seamless log collection
- Advanced filtering options to narrow down specific areas of interest within the logs