AI Analysis
The package exhibits minimal risk indicators across all checks performed, suggesting it is unlikely to pose a threat. The metadata risk score is slightly elevated due to low activity and effort, but this alone does not indicate malicious behavior.
- No network or shell risks detected
- Minimal obfuscation and credential risks
- Low metadata activity
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on local debugging.
- Shell: No shell executions detected, consistent with a benign utility package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The package shows signs of low activity and metadata effort, but there are no clear red flags indicating malicious intent.
Package Quality Overall: Low (1.4/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
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
Single-author or unverifiable project
1 unique contributor(s) across 5 commits in andreamirarchi/auditory_debuggerSingle author with few commits — possibly a personal or throwaway project
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 andreamirarchi/auditory_debugger appears legitimate
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Andrea Mirarchi" 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 Python-based audio-driven debugging tool called 'AudioWatch'. This tool will help developers debug their code by providing real-time feedback through sound cues instead of just visual notifications. The application should have the following functionalities: 1. **Integration with Auditory Debugger**: Utilize the 'auditory-debugger' package to set up sound alerts at specific breakpoints in the code. 2. **Customizable Sound Cues**: Allow users to choose from a variety of sound effects or upload their own audio files to use as debugging signals. 3. **Real-Time Monitoring**: Implement a feature that monitors the execution flow of a program and triggers sound alerts when it hits predefined breakpoints. 4. **Watchdog Timer**: Incorporate a watchdog timer that emits an alert if the program execution exceeds a certain time limit, helping to identify performance bottlenecks. 5. **User Interface**: Develop a simple and intuitive UI using Tkinter or any other preferred Python GUI library, which allows users to configure settings such as breakpoints and timeout limits. 6. **Logging Mechanism**: Include a logging mechanism that records all events (sound alerts, timeouts, etc.) for post-run analysis. 7. **Compatibility**: Ensure the tool works seamlessly with Python scripts and popular IDEs like PyCharm and VSCode. The goal is to create a user-friendly and efficient debugging tool that enhances the developer's experience by leveraging auditory feedback alongside traditional visual cues.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue