AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity such as network calls, shell executions, obfuscation, or credential harvesting. The metadata risk is slightly elevated due to the author having only one package, but this alone does not suggest a supply-chain attack.
- No network calls detected
- No shell execution patterns
- No obfuscation patterns
- No credential harvesting patterns
- Metadata risk due to single package by author
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access to function.
- Shell: No shell execution patterns detected, indicating the package likely does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which may indicate a new or less active account, but no other red flags were identified.
Package Quality Overall: Low (4.6/10)
○ 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 (21022 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
35 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
6 unique contributor(s) across 100 commits in awslabs/amazon-omics-toolsActive community — 5 or more distinct contributors
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
Repository awslabs/amazon-omics-tools appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Amazon Web Services" 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 aws-healthomics-tools
Create a patient health data analysis tool using the 'aws-healthomics-tools' Python package. This tool will allow healthcare professionals to upload patient health data from various sources, analyze it for trends and anomalies, and generate reports summarizing key findings. The application should be designed to work seamlessly with the AWS HealthOmics service, leveraging its capabilities to process large volumes of health data efficiently. **Core Features:** 1. **Data Upload:** Users should be able to upload patient health data in CSV format. The data should include fields such as patient ID, timestamp, vital signs, lab results, etc. 2. **Data Processing:** Utilize the 'aws-healthomics-tools' package to process the uploaded data. This includes cleaning the data, handling missing values, and normalizing the data format. 3. **Trend Analysis:** Implement trend analysis on the processed data to identify patterns over time. For example, detecting if a patient's blood pressure has been consistently high over several months. 4. **Anomaly Detection:** Use anomaly detection algorithms provided by the 'aws-healthomics-tools' package to flag any unusual readings or sudden changes in patient health metrics. 5. **Report Generation:** Generate comprehensive reports based on the analysis performed. These reports should highlight key trends, anomalies, and any potential health risks identified. 6. **Visualization:** Include visual representations of the analyzed data, such as graphs showing trends over time, to make the information more accessible and understandable. **Additional Features (Optional):** - Integration with AWS S3 for storing uploaded files securely. - Real-time monitoring dashboard for tracking ongoing patient health metrics. - Alert system to notify healthcare providers of significant anomalies or trends. The application should be built with user-friendliness in mind, ensuring that healthcare professionals without extensive technical knowledge can easily use it. Additionally, ensure that all data handling processes comply with HIPAA regulations and best practices for patient data privacy.
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