AI Analysis
The package has minimal risks associated with network calls, shell execution, and obfuscation, but its metadata suggests it might be poorly maintained, raising some concerns about its reliability and potential for future issues.
- Metadata risk due to signs of being new and potentially not well maintained.
- Low risk in other categories.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external services.
- Shell: No shell execution detected, reducing the risk of executing unauthorized commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of being new and potentially not well maintained, raising suspicion.
Package Quality Overall: Low (3.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (1805 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Active multi-contributor project
4 unique contributor(s) across 24 commits in brandonzorn/link-shortenersSmall but multi-author team (3–4 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
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 web-based content moderation tool using Flask, a lightweight WSGI web application framework in Python. This tool will help users check if links they post contain any spam URL shortener domains, which could potentially harm their website's reputation or redirect users to malicious sites. Utilize the 'antispam-link-shorteners' package to identify such domains and provide real-time feedback to users about the safety of the links they input. Step-by-Step Guide: 1. Set up a Flask environment and install necessary packages including 'antispam-link-shorteners'. 2. Design a simple HTML form where users can paste URLs they want to verify. 3. Implement a backend function that takes the URL from the form, uses 'antispam-link-shorteners' to check if it contains any known spam domains, and returns a result indicating whether the link is safe or not. 4. Display the verification result back to the user on the same page. 5. Add caching mechanisms provided by 'antispam-link-shorteners' to speed up repeated checks for common domains. 6. Optionally, allow users to report suspicious links directly through the tool, which could then be added to a database for future reference. 7. Ensure the tool has a clean, user-friendly interface and is responsive across different devices. Features: - Real-time verification of URL safety. - Reporting mechanism for new potential spam domains. - Caching to improve performance. - User-friendly design. How 'antispam-link-shorteners' is utilized: - The package's core functionality is leveraged to scan submitted URLs against a database of known spam domains. - Its caching feature helps in reducing the load time for frequently checked domains, enhancing the overall user experience. - Users benefit from immediate feedback on the safety of the links they plan to share, thereby protecting both themselves and others from potential threats.
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