AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of potential obfuscation and has metadata risks due to its newness and lack of maintainer history.
- Obfuscation risk detected
- Low maintainer activity and history
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
- Obfuscation: The patterns detected suggest obfuscation of code but do not indicate malicious intent; they appear to be related to model evaluation and tensor operations.
- Credentials: No patterns indicative of credential harvesting or secret handling were detected.
- Metadata: The package is suspicious due to its newness, lack of maintainer history, and minimal repository engagement.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 10.0
Found 6 obfuscation pattern(s)
-> torch.Tensor: self.eval() use_bilinear = self.supervised_config.head_type =="], strict=True) wrapper.eval() return wrapper class NeuralReactionMapper(Reacti""" self._model.eval() enc = self._tokenizer( text,ss": 0.0} self.model.eval() total_loss = 0.0 total_mlm_loss = 0.0""" self.model.eval() input_ids = input_ids.to(self.device) atte) model.eval() total_attention_loss = 0.0 num_batches = 0
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: denovochem.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 agave-chem
Create a chemical reaction analysis tool using the Python package 'agave-chem'. This tool will allow users to input a chemical reaction equation and receive detailed classifications and atom mappings of the reaction. Here are the steps and features your project should include: 1. **Setup**: Begin by installing 'agave-chem' and any other necessary packages such as pandas for data handling. 2. **User Interface**: Develop a simple command-line interface (CLI) where users can input their chemical reactions. Ensure the input format is user-friendly and allows for easy correction of errors. 3. **Reaction Parsing**: Use 'agave-chem' to parse the inputted reaction equation and extract relevant information like reactants, products, and catalysts. 4. **Classification**: Implement functionality that classifies the type of chemical reaction based on the parsed information. Use 'agave-chem' capabilities for reaction classification. 5. **Atom Mapping**: Provide atom mapping details for each molecule involved in the reaction. This will help users understand how atoms move from reactants to products. 6. **Output Presentation**: Display the results in a clear and organized manner. Include visual representations if possible, such as molecule diagrams using another library like RDKit. 7. **Error Handling**: Add robust error handling to manage incorrect inputs and provide meaningful feedback to the user. 8. **Documentation**: Write comprehensive documentation detailing how to use the CLI, common issues, and how to interpret the output. By following these steps, you'll create a valuable tool for chemists and students looking to better understand and analyze chemical reactions.