atlas-agent

v1.0.11 suspicious
6.0
Medium Risk

CLI to operate Atlas via gRPC, with LLM-powered planning, tooling, and verification.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, and obfuscation activities, but the metadata raises concerns due to the maintainer having only one package and an untraceable git repository.

  • Maintainer has only one package
  • Git repository not found
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package and the git repository is not found, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8736 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Feng Lab" 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 atlas-agent 
Create a command-line utility called 'AtlasBot' that leverages the 'atlas-agent' package to automate the deployment and management of microservices in a Kubernetes cluster. The utility should have the following core functionalities:

1. **Deployment Automation**: Users should be able to specify a YAML file containing Kubernetes deployment configurations. The utility will then use the 'atlas-agent' package to plan and execute the deployment of these services into the Kubernetes cluster.
2. **Service Verification**: After deploying a service, the utility should automatically verify its health status using the 'atlas-agent' package's verification capabilities. This includes checking if the service is running, its readiness probes, and its liveness probes.
3. **Tooling Integration**: Integrate third-party tools like Prometheus for monitoring and Grafana for visualization into the deployment process. Use 'atlas-agent' to manage the installation and configuration of these tools alongside the microservices.
4. **Dynamic Scaling**: Implement a feature where users can dynamically scale their services based on load metrics. The utility should monitor these metrics and adjust the number of replicas accordingly, again leveraging 'atlas-agent' for the planning and execution of scaling operations.
5. **User-Friendly Interface**: Ensure the utility has a clear and user-friendly CLI interface. Commands should include options for specifying Kubernetes contexts, deployment files, and verification parameters.
6. **Logging and Reporting**: Provide logging and reporting mechanisms that allow users to track the progress of deployments, verifications, and scaling operations. Logs and reports should be stored locally and also accessible via a web dashboard that you will develop as part of this utility.

To achieve these functionalities, utilize the 'atlas-agent' package for its gRPC-based operations, LLM-powered planning, and automated verification processes. Your goal is to create a comprehensive tool that simplifies the management of Kubernetes-based microservices environments.

💬 Discussion Feed

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