aicostguard-dev

v0.0.1 suspicious
5.0
Medium Risk

Placeholder reserving the name for AI Cost Guard auto-instrumentation. The real package ships in v0.3.0b1+ — see homepage.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is flagged as suspicious due to its placeholder content and lack of actual functionality until a later version. Additionally, it has a non-existent git repository and a questionable maintainer history, which raises concerns about its legitimacy.

  • Suspicious maintainer history
  • Non-existent git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or system compromise.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package has a suspicious maintainer history and a non-existent git repository, indicating potential risk.

📦 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

  • Brief PyPI description (598 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

Email domain looks legitimate: gmail.com>

✓ 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 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 aicostguard-dev 
Create a mini-application called 'CostGuardCLI' that helps developers manage and monitor their AI model costs in real-time. This application will use the 'aicostguard-dev' Python package to automatically instrument various machine learning workflows, allowing users to understand and control their cloud computing expenses more effectively.

The application should have the following core functionalities:
- Integration with popular cloud providers such as AWS, GCP, and Azure.
- Real-time monitoring of AI model execution costs.
- Automatic cost alerts when spending exceeds predefined thresholds.
- Historical cost analysis with visualizations.
- Support for multiple AI frameworks including TensorFlow, PyTorch, and Scikit-Learn.

Steps to create the application:
1. Set up the development environment with Python and install the necessary packages, including 'aicostguard-dev'.
2. Design a user-friendly CLI interface using Click or argparse for easy command-line interaction.
3. Implement cloud provider integration by configuring authentication through API keys or IAM roles.
4. Use 'aicostguard-dev' to automatically track and report on the costs associated with running AI models.
5. Develop real-time alerting functionality based on cost thresholds set by the user.
6. Create a historical cost analysis feature that allows users to review past expenses and generate visual reports.
7. Test the application thoroughly across different AI frameworks and cloud environments to ensure reliability.
8. Document the setup process, usage instructions, and troubleshooting tips for new users.
9. Deploy the application as a standalone executable or containerized service for easy distribution.

The 'aicostguard-dev' package is utilized throughout the application to handle the automatic instrumentation of AI model executions, providing granular cost data which is then processed and presented to the user via the CLI.