🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some concerning metadata indicators such as lack of maintainer history and recent upload, suggesting potential risk. However, it does not exhibit any immediate signs of malicious activity like network calls or shell execution.

Lack of maintainer history and recent upload time indicate potential risk.
No network calls, shell executions, or obfuscation patterns detected.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
Metadata: The package shows several red flags including lack of maintainer history and a recent upload time, suggesting potential risk.

🔬 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 10.0

5 maintainer concern(s) found

Only one version has ever been released — brand new package
Package uploaded less than 24 hours ago (2026-06-05T09:53:47.000Z)
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with compiler-claw

Create a mini-application named 'NeuralTransfer' that leverages the 'compiler-claw' package to perform end-to-end migration of neural network models from GPU to NPU and accelerates inference on NPUs. The application should have a user-friendly interface allowing users to upload their pre-trained models in popular frameworks like TensorFlow or PyTorch, select the target NPU architecture, and initiate the migration process. Once the migration is complete, the application should allow users to test the performance of the model on the NPU through a simple inference task. Key features include:

1. Model Import: Allow users to import their pre-trained models using APIs or a file upload feature.
2. Architecture Selection: Provide a dropdown menu for selecting different NPU architectures supported by 'compiler-claw'.
3. Migration Process: Implement a seamless migration process that utilizes 'compiler-claw' to convert the model from GPU to NPU format.
4. Performance Testing: After migration, enable users to run basic inference tasks on the NPU to evaluate performance improvements.
5. Reporting: Generate a report detailing the time taken for migration and inference speedup achieved on the NPU compared to the original GPU setup.

Ensure that your application showcases the capabilities of 'compiler-claw' in simplifying the transition between hardware platforms while enhancing computational efficiency.