AI Analysis
The package amesa-api v0.30.0 has minimal risks associated with it based on the analysis. It does not engage in risky behaviors such as making network calls or executing shell commands.
- No network calls detected
- No shell execution patterns detected
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external API access.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands which reduces the risk of malicious activity.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (1344 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: amesa.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a financial tracking mini-application using the Amesa API package in Python. This application will allow users to track their financial transactions, categorize expenses, and generate reports based on transaction history. Hereβs a detailed breakdown of the steps and features to implement: 1. **User Authentication**: Implement a secure login system where users can create accounts and log in securely. Use the Amesa API to fetch user-specific financial data. 2. **Transaction Fetching**: Utilize the Amesa API to fetch the latest financial transactions from the user's account. Ensure the transactions include details such as date, amount, category, and description. 3. **Categorization System**: Develop a feature that allows users to categorize their transactions into predefined categories like groceries, utilities, entertainment, etc. This should be flexible enough to allow users to add custom categories if needed. 4. **Report Generation**: Create a reporting feature that generates monthly summaries of spending. Users should be able to view these summaries either as a detailed report or a simple chart. 5. **Budget Management**: Integrate a budget management system where users can set monthly budgets for each category and receive alerts when they are nearing or exceeding their budget limits. 6. **Data Visualization**: Incorporate graphs and charts to visually represent the transaction data. For instance, show a pie chart representing the distribution of expenses across different categories. 7. **Security Measures**: Ensure all sensitive data is handled securely, following best practices for data protection and privacy. 8. **User Interface**: Design a clean, intuitive user interface for ease of use. Consider both command-line and graphical interfaces. By utilizing the Amesa API, your application will leverage real-time financial data to provide personalized insights and tools for better financial management.
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