AI Analysis
The package appears to be safe with very low risks across all categories. It shows minimal signs of being well-maintained, but there are no indications of malicious activity or supply-chain attacks.
- No network calls or shell executions detected.
- Low risk for obfuscation and credential harvesting.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- 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 package shows signs of low maintenance and metadata quality, but there are no explicit red flags indicating malicious intent.
Package Quality Overall: Low (3.4/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (4840 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
Active multi-contributor project
3 unique contributor(s) across 100 commits in AustinKong/attoSmall but multi-author team (3β4 contributors)
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
No author email provided
All external links appear legitimate
Repository AustinKong/atto appears legitimate
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 fully-functional local-first job application tracker using the 'atto-app' Python package. This mini-app will allow users to manage their job applications locally without needing internet access. Hereβs a step-by-step guide on what your application should achieve and some suggested features: 1. **Setup**: Begin by installing the 'atto-app' package. Ensure you set up a local database for storing job application details. 2. **User Interface**: Develop a clean, intuitive UI that allows users to add new job applications, view existing ones, and edit or delete them. 3. **Application Details**: Each job application should include fields such as company name, job title, date applied, status (e.g., Applied, Interview Scheduled, Rejected), notes, and any attachments (e.g., cover letter). 4. **Search Functionality**: Implement a search bar where users can find specific job applications by keywords from the company name, job title, or notes. 5. **Status Tracking**: Allow users to easily track the status of each application through a dropdown menu or similar interface element. 6. **Notifications**: Add a feature that reminds users when it's time to follow up on a particular application based on the date they applied. 7. **Export Data**: Provide an option to export all job application data to a CSV file for backup purposes. 8. **Security Considerations**: Since the app runs locally, ensure that user data is securely stored and not accessible to unauthorized users. 9. **Testing**: Thoroughly test the application to ensure all features work correctly and efficiently. Use the 'atto-app' package to handle the backend logic, including data storage, retrieval, and manipulation. Your goal is to create a robust, user-friendly tool that simplifies the process of managing job applications.
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