ai-agent-rules

v0.62.2 suspicious
5.0
Medium Risk

Manage user-level AI agent configurations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential unauthorized system interactions from executing shell commands and some uncertainty around the maintainer's identity and activity level.

  • Execution of shell commands poses a significant risk.
  • Missing author information and low package activity raise suspicion.
Per-check LLM notes
  • Network: Network calls appear to be checking for updates and fetching tags, which is somewhat common but should be scrutinized for legitimacy.
  • Shell: Execution of shell commands, especially external tools like 'uv', raises concerns about potential unauthorized system interactions or data manipulation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's name is missing and the author has only one package, which may indicate a new or less active maintainer, raising some suspicion.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (17380 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

  • 297 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in wpfleger96/ai-agent-rules
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • /CHANGELOG.md" req = urllib.request.Request(url) req.add_header("User-Agent", f"ai-rules
  • rent_version}") with urllib.request.urlopen(req, timeout=timeout) as response: chang
  • s/{repo}/tags" req = urllib.request.Request(url) req.add_header("User-Agent", f"ai-rules
  • rent_version}") with urllib.request.urlopen(req, timeout=timeout) as response: data
  • oject.toml" req = urllib.request.Request(url) req.add_header("User-Agent", "ai-ru
  • "ai-rules") with urllib.request.urlopen(req, timeout=timeout) as response: d
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • }" try: result = subprocess.run( cmd, capture_output=True,
  • e] try: result = subprocess.run( cmd, capture_output=True,
  • ne try: result = subprocess.run( ["uv", "tool", "list"], capture_out
  • index_url]) result = subprocess.run( cmd, capture_output=True,
  • ]) try: result = subprocess.run( cmd, capture_output=True,
  • efault" result = subprocess.run( [ "ai-agent-rules",
✓ 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

Repository wpfleger96/ai-agent-rules appears legitimate

⚠ 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 ai-agent-rules 
Create a fully functional mini-application called 'AI Configurator' that leverages the Python package 'ai-agent-rules' to manage AI agent configurations for different users. The application should allow users to create, edit, delete, and view their AI agent settings, such as preferred language models, response formats, and interaction modes. Additionally, the app should support the ability to set up custom rules for specific scenarios, like enabling or disabling certain features based on the context of the interaction.

Step-by-step instructions:
1. Set up the initial project structure and install the 'ai-agent-rules' package.
2. Design a simple UI for the application using a library like Tkinter or Streamlit for a desktop or web interface respectively.
3. Implement functions to add new user profiles and assign default AI agent configurations.
4. Allow users to customize their AI agent settings through the UI, utilizing the 'ai-agent-rules' package to store and retrieve these configurations.
5. Develop a feature to apply custom rules to the AI agent configurations, enabling advanced control over the behavior of the AI agents based on predefined conditions.
6. Integrate error handling and validation checks to ensure data integrity and user-friendly experience.
7. Test the application thoroughly to ensure all functionalities work as expected.
8. Document the code and provide clear instructions for running the application.

Suggested Features:
- User authentication for secure access to individual configurations.
- A history log of changes made to each user's AI agent settings.
- Integration with popular language models to showcase different configuration effects.
- Export and import options for user configurations to allow for easy backup and transfer.