AI Analysis
The package appears to be safe with low risks across most categories. The only notable concern is the presence of a SECRET_KEY check which might indicate potential credential handling issues.
- moderate credential risk due to SECRET_KEY check
- low risk in network, shell, and obfuscation activities
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 no direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected in the code snippet provided.
- Credentials: The code is checking for a 'SECRET_KEY' which could be a legitimate practice for securing applications, but the absence of context around how it's used raises a moderate risk for potential credential harvesting.
- Metadata: The maintainer has only one package and contains non-HTTPS links which could be local placeholders, but no immediate signs of typosquatting or other malicious intent.
Package Quality Overall: Medium (6.4/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Documentation URL: "Documentation" -> https://nagarjuna-tella.github.io/Aksara/Detailed PyPI description (23104 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
Classifier: Typing :: Typed671 type-annotated function signatures detected in source
Active multi-contributor project
3 unique contributor(s) across 100 commits in nagarjuna-tella/AksaraSmall but multi-author team (3–4 contributors)
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
Found 1 credential access pattern(s)
n.get("AKSARA_SECRET_KEY") or os.environ.get("SECRET_KEY") if not secret: issues.append(Diag
No typosquatting candidates detected
No author email provided
Found 3 suspicious link(s) on the package page
Non-HTTPS external link: http://127.0.0.1:8000/docsNon-HTTPS external link: http://127.0.0.1:8000/studio/uiNon-HTTPS external link: http://127.0.0.1:8000/ai/tools/mcp
Repository nagarjuna-tella/Aksara appears legitimate
1 maintainer concern(s) found
Author "Nagarjuna Tella" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a fully-functional mini-app that serves as a personal task manager using the 'aksara-framework' Python package. This app will allow users to manage their daily tasks efficiently, including adding new tasks, marking tasks as completed, and deleting tasks. Additionally, it will feature user authentication to ensure each user has their own private task list. Steps to Build the App: 1. Setup the environment: Install Python and the necessary packages, including 'aksara-framework'. 2. Design the database schema: Define tables for storing user information and tasks. 3. Implement user authentication: Use 'aksara-framework' to handle user registration and login functionalities securely. 4. Develop CRUD operations for tasks: Utilize 'aksara-framework' to create, read, update, and delete tasks. 5. Enhance the app: Add features like setting due dates for tasks and categorizing them. 6. Test the application: Ensure all functionalities work as expected. 7. Deploy the app: Publish the app on a cloud platform of your choice. Features to Include: - User registration and login. - Secure password storage and management. - Adding new tasks with titles and descriptions. - Marking tasks as completed. - Deleting tasks. - Setting due dates for tasks. - Categorizing tasks into different categories (e.g., Work, Personal). - Displaying a list of all tasks. - Filtering tasks based on completion status and categories. How 'aksara-framework' is Utilized: - For handling asynchronous requests and responses, making the app more responsive. - For building RESTful APIs to interact with the backend services. - For managing database connections and executing SQL queries efficiently. - For securing API endpoints with authentication and authorization mechanisms.