agentsmd

v1.2.1 suspicious
5.0
Medium Risk

CLI tool for scaffolding and syncing AI agent workspace configuration files

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential obfuscation practices and lack of supporting metadata such as a linked GitHub repository.

  • High obfuscation risk through base64 encoding
  • No associated GitHub repository for the maintainer
Per-check LLM notes
  • Network: The presence of network calls is common for packages that require interaction with external services, but should be reviewed to ensure it aligns with the package's purpose.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of base64 encoding to decode and write content suggests potential obfuscation practices.
  • Credentials: No clear evidence of credential harvesting patterns.
  • Metadata: The maintainer has only one package and no associated GitHub repository, which could indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://agentsmd.live/docs/cli
  • Detailed PyPI description (4798 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

  • 171 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ) self._client = httpx.AsyncClient( base_url=self.base_url, headers={"A
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • file_path.write_bytes(base64.b64decode(item["content"])) count += 1 return count """Au
  • conflict["path"] local = base64.b64decode(conflict["local_content"]) cloud = base64.b64decode(conf
  • "local_content"]) cloud = base64.b64decode(conflict["cloud_content"]) if choice.upper() == "L":
  • = "both": cloud = base64.b64decode(conflict["cloud_content"]) parts = path.rsplit("
  • in conflicts: cloud = base64.b64decode(conflict["cloud_content"]) resolutions.append((confl
  • "path"] local_bytes = base64.b64decode(c["local_content"]) cloud_bytes = base64.b64decode(c
✓ 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: gentlabs.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Harshith: Admin" 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 agentsmd 
Your task is to create a Python-based mini-application named 'AgentSyncer', which leverages the 'agentsmd' package to manage and synchronize configurations of multiple AI agents across different environments. This application will serve as a powerful tool for developers and data scientists working on AI projects that involve managing multiple AI agent configurations.

The core functionality of 'AgentSyncer' should include:
1. **Scaffolding**: Automatically generate a basic directory structure and configuration files needed for an AI agent workspace.
2. **Configuration Synchronization**: Sync configuration changes made in one environment (e.g., development) to other environments (e.g., testing, production).
3. **Version Control Integration**: Integrate with version control systems like Git to track changes in configurations over time.
4. **Environment Management**: Allow users to switch between different environments (development, testing, production) seamlessly.
5. **Security Features**: Implement basic security measures such as encryption of sensitive configuration details.

To achieve these functionalities, you will utilize the 'agentsmd' package in the following ways:
- Use 'agentsmd' to scaffold the initial workspace setup when the user initializes a new AI agent project.
- Leverage 'agentsmd' to sync configuration files between different environments, ensuring consistency across all setups.
- Incorporate 'agentsmd' commands into your application to facilitate easy management of AI agent workspaces.

Additionally, consider adding a feature that allows users to customize their workspace setup through command-line arguments or configuration options. This will enhance the flexibility and usability of 'AgentSyncer'.

Your final deliverable should include a well-documented Python script, along with any necessary configuration files and documentation on how to install and use 'AgentSyncer'. Make sure to include examples and best practices for integrating 'agentsmd' into existing workflows.