🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators with no network or shell risks detected. The metadata suggests a new maintainer but does not raise significant red flags.

No network calls detected.
No shell execution patterns observed.
Maintainer has only one package on PyPI.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no direct system command risks.
Metadata: The maintainer has only one package on PyPI, which might indicate a new or less active account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository lpalbou/abstractframework appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Laurent-Philippe Albou" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with AbstractMemory

Develop a mini-application called 'TemporalJournal' that leverages the 'AbstractMemory' package to create a personalized, temporal journaling system. This system should allow users to log their daily activities, thoughts, and events with timestamps, providing a comprehensive record of their life over time. Additionally, the application should support querying past entries based on specific dates, keywords, or even sentiment analysis to find relevant memories. Utilize 'AbstractMemory' to ensure that each entry is stored with its timestamp and provenance information, allowing for accurate retrieval and analysis of past data. Here are the key features and steps to implement this application:

1. **User Interface Design**: Create a simple yet intuitive UI where users can easily input new journal entries and view their past logs.
2. **Entry Logging**: Implement functionality to log new journal entries, including text input fields and a timestamp auto-generator using 'AbstractMemory'. Each entry should also include metadata such as location, mood, and any other relevant tags.
3. **Provenance Tracking**: Use 'AbstractMemory' to track the provenance of each entry, ensuring that modifications or deletions are recorded alongside the original entry.
4. **Query Functionality**: Develop search capabilities within the application that allow users to query past entries based on date ranges, keywords, and sentiment. Utilize 'AbstractMemory' to efficiently retrieve entries based on these criteria.
5. **Sentiment Analysis**: Integrate a basic sentiment analysis tool to categorize each entry as positive, neutral, or negative. This will enhance the querying feature by allowing users to explore their emotional journey over time.
6. **Visualization Tool**: Add a visualization component that uses the logged data to generate graphs and charts showing trends in user activity, mood, etc., over time.
7. **Security and Privacy**: Ensure that all user data is stored securely and that users have control over who can access their journals. Consider implementing encryption for sensitive data.
8. **Testing and Validation**: Thoroughly test the application to ensure it functions correctly and efficiently retrieves and stores data as expected. Validate the sentiment analysis accuracy and the effectiveness of the provenance tracking.