AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity with low scores across all risks assessed. However, the incomplete maintainer information and potential inactivity warrant some caution.
- Incomplete maintainer information
- Potential inactivity of the maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external API access.
- Shell: No shell execution detected, which is expected as typical Python packages do not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The maintainer's author information is incomplete, and they appear to be new or inactive.
Package Quality Overall: Medium (7.0/10)
✦ High
Test Suite
9.0
Test suite present — 1 test file(s) found
Test runner config found: pyproject.toml1 test file(s) detected (e.g. testcase.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://amazon-orders.readthedocs.ioDetailed PyPI description (6459 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
120 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
6 unique contributor(s) across 100 commits in alexdlaird/amazon-ordersActive 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
Email domain looks legitimate: alexlaird.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository alexdlaird/amazon-orders appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author 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 amazon-orders
Create a Python-based personal finance tool called 'AmazonBudget' that leverages the 'amazon-orders' package to provide users with a detailed analysis of their Amazon spending habits. This tool will allow users to authenticate via OAuth, retrieve their order history from Amazon, and then perform various analyses on the data such as total spent per month, most frequently purchased categories, and average order value. Key Features: 1. User Authentication: Implement a secure method for users to authenticate with their Amazon account using OAuth. 2. Order Retrieval: Utilize the 'amazon-orders' package to fetch the user's order history over a specified period. 3. Data Analysis: Provide insights into spending patterns by calculating monthly expenditure, identifying top categories of purchases, and determining average order values. 4. Visualization: Integrate a simple plotting library like matplotlib or seaborn to visualize spending trends over time. 5. Reporting: Generate a monthly report in PDF format summarizing key metrics such as total spend, top categories, and any anomalies detected. 6. Notifications: Send email notifications when certain thresholds are reached (e.g., spending more than $500 in a single month). Utilization of 'amazon-orders': - Use 'amazon-orders' to authenticate and securely access the user's Amazon order history. - Parse the order data to extract relevant information such as order date, category, item price, and shipping cost. - Store the retrieved order data locally or in a database for further processing and analysis.