agentcfg

v0.2.1 suspicious
4.0
Medium Risk

CLI tool for deploying and managing AI coding agent configurations (MCP servers, skills, instructions) across multiple providers.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some legitimate operational activities but raises concerns due to potential shell execution risks and unusual commit patterns, suggesting the need for closer scrutiny.

  • Shell risk detected that requires further investigation.
  • Recent burst of commits and lack of maintainer history indicate metadata risk.
Per-check LLM notes
  • Network: No network calls were detected.
  • Shell: Detected shell execution may be part of normal operations like version control interactions, but requires further investigation to confirm legitimacy.
  • Obfuscation: The detected patterns are likely for logging purposes, not malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The recent burst of commits and lack of maintainer history suggest potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • separator, f"{__import__('datetime').datetime.now():%Y-%m-%d %H:%M:%S} Agent Config Deploy{' [D
  • separator, f"{__import__('datetime').datetime.now():%Y-%m-%d %H:%M:%S} Agent Config Pull{' [DRY
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • ss-platform. result = subprocess.run( cmd, capture_output=True,
  • hs] try: result = subprocess.run( ["git", "add", "--"] + str_paths, c
  • om/amtiYo/agents result = subprocess.run( ["powershell.exe", "-c", "& (Get-Command agents -Er
  • y by # subprocess without shell=True. shutil.which() respects PATHEXT and # returns the ful
  • turn True try: # shell=True is required on Windows when the resolved executable is a
✓ 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: All 8 commits happened within 24 hours

  • All 8 commits happened within 24 hours
⚠ 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 agentcfg 
Create a Python-based mini-application called 'AIConfigurator' that leverages the 'agentcfg' package to streamline the deployment and management of AI coding agents across various cloud providers. This application should allow users to easily configure, deploy, and manage different AI coding agents by specifying MCP server details, skill sets, and specific instructions.

The application should include the following core functionalities:
1. **Configuration Management**: Users should be able to define and save configurations for multiple AI coding agents, including MCP server URLs, authentication tokens, and a list of skills and instructions.
2. **Deployment**: Once configurations are saved, users should have the ability to deploy these configurations to one or more cloud providers using the 'agentcfg' package's deployment capabilities.
3. **Monitoring and Updating**: After deployment, the application should provide tools for monitoring the status of deployed agents and updating their configurations as needed.
4. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. The CLI should support basic commands like 'configure', 'deploy', 'update', and 'monitor'.
5. **Security Features**: Ensure that sensitive information such as authentication tokens are securely stored and managed within the application.

In your implementation, make sure to utilize the 'agentcfg' package's features effectively. For example, use its CLI capabilities to handle the deployment process, and leverage its configuration management features to store and retrieve agent settings. Additionally, consider adding optional advanced features such as automated backups of configurations, support for multiple user profiles, and integration with popular cloud provider APIs for enhanced functionality.